**A National Strategy Proposal for Improved Cooking Stove Adoption in Honduras: Energy Consumption and Cost-Benefit Analysis**

#### **Wilfredo C. Flores 1, Benjamin Bustamante 2, Hugo N. Pino 1, Ameena Al-Sumaiti <sup>3</sup> and Sergio Rivera 4,\***


Received: 30 December 2019; Accepted: 12 February 2020; Published: 19 February 2020

**Abstract:** The high consumption of firewood in Honduras necessitates the search for alternatives with less-negative effects on health, the economy, and the environment. One of these alternatives has been the promotion of improved cooking stoves, which achieve a large reduction in firewood consumption. This paper presents a cost-benefit analysis for an improved cooking stove adoption strategy for Honduras. The methodology uses the *Long-range Energy Alternatives Planning System*, LEAP, a tool used globally in the analysis and formulation of energy policies and strategies. The energy model considers the demand for firewood as well as the gradual introduction of improved cooking stoves, according to the premises of a National Strategy for improved cooking stoves adoption in Honduras. Hence, it is demonstrated that the costs of implementing this adoption strategy are lower than the costs of not implementing it, taking into consideration representative scenarios up to and including the year 2030.

**Keywords:** cost-benefit analysis; energy strategy; improved cook stoves; Honduras

#### **1. Introduction**

Firewood is a very important source of energy in Honduras [1]. Many households with access to electricity still use firewood as the main source of energy for cooking food. Firewood is also used in micro and medium enterprises dedicated to the sale of food, salt extraction, brick production, bakeries, tortilla manufacturing, and coffee mills, among others. In urban and peri-urban areas, 29% of households use firewood, while in rural areas firewood continues to predominate in 88% of households [1]. Hence, in the last few decades there has been a significant increase in deforestation in Honduras. Studies reveal that the volume loss per year is 58,000 hectares. In 2015, after a period of 17 years, the forest reduction was 870,000 hectares [2].

Energy is essential for human development in various ways, such as health care, transportation, information, communication, lighting, heating, food processing, and other uses. Therefore, energy poverty has serious implications for basic human needs, such as cooking, heating the home, lighting or access to basic media services.

In the Honduran case, according to the use of energy in households, the total number of basic energy needs is six. Figure 1 shows these six groups of basic energy needs for Honduran households.

**Figure 1.** Basic energy needs for Honduran households.

The majority of poor countries around the world use firewood to meet some of these needs, mainly for cooking. In many cases, the use of biomass is not the most appropriate or suitable in terms of implications for health, and they are not precisely cheaper sources, but they tend to be the only option available. Despite the potential of technologies such as solar ovens [3] and others to be useful, a large quantity of developing countries still use firewood for cooking.

About half of Honduran households (approximately one million) cook with traditional wood-burning stoves [1,4]. These stoves are not only inefficient, but also have highly detrimental effects on the health of the user. In addition, the cost of collecting or buying firewood also has a huge impact on the economy and social welfare of families.

Consequently, the high consumption of firewood in Honduras requires the search for alternatives that reduce its negative impacts. In the country, one of these alternatives has been the promotion of improved stoves. This adoption achieves a large reduction in firewood consumption, as improved stoves can potentially use up to 71.2% less wood than traditional stoves, depending on the technology and user [4]. Additionally, families cooking with a traditional stove in zones where it is difficult to find firewood (peri-urban areas) spend about USD 20.00 per month on firewood purchases. Furthermore, also exists the cost of travel and time for its collection. Additionally, it is necessary to take the health expenses of respiratory diseases associated with the traditional stoves into account [5,6]. Figure 2 shows traditional (**a**) and improved stoves (**b**) used in Honduras.

However, the country programs that introduced improved stoves have traditionally been isolated efforts, with few resources for technological development and with a lack of follow-up on the adoption of new technologies [6]. Additionally, the adoption of improved stoves in Honduran households has been affected by a lack of public policies or strategies with a long-term vision for the development of a value chain that integrates the different links, such as design, manufacturing, financing, marketing, and post-sales services, as well as the sustainable supply of wood [7].

In this way, a change of direction is required; it calls for a comprehensive and joint strategy that allows the use of improved stoves to develop under different conditions. This strategy must be economically viable. Prior to its development, it is essential to perform a cost-benefit analysis of the strategy implementation. Similar analyses—completed in other countries (specify) using varying methodologies—have shown that the implementation of improved stoves is viable [6,7]. This paper reinforces the conclusion of the feasibility of technology presented in [6], but using a different methodology and the assumptions of a National Technology Adoption Strategy.

**Figure 2.** Traditional and improved stoves used for cooking in Honduras. (https://envirofit.org/ honduras/). (**a**) Traditional firewood stove (**b**) Improved stove.

Thus, the methodology implemented included a review of literature and interviews with the stakeholders of the improved stoves value chain in Honduras. For the cost-benefit analysis, the *Long-range Energy Alternatives Planning System*, LEAP® (Software version: 2018.1.37, Stockholm Environment Institute. Somerville, MA, USA) tool was used [8]. This tool is widely used in the analysis and formulation of energy policies and strategies worldwide. This tool considers the demand for firewood, as well as the gradual introduction of improved stoves for cooking food, according to the assumptions of a National Technology Adoption Strategy.

#### **2. Material and Methods**

#### *2.1. Current Status of Improved Stoves Delivery for a National Strategy Adoption in Honduras*

This subsection presents the stakeholders, projects, and the NAMA (Nationally Appropriate Mitigation Actions) program according to a national strategy for the adoption of improved stoves in Honduras.

#### 2.1.1. Stakeholders and Projects

The companies dedicated to the promotion and construction of improved stoves are currently small, non-profit or growing social enterprises with minimal capital, which basically depend on sales through contracts signed with non-governmental organizations, who in turn depend mainly on donations from small local or international initiatives. There is neither a wide market for improved stoves, nor any chance of one being generated if the state continues giving away the stoves [9,10].

A case to highlight this concept is the Mirador project, which finances part of its activities using carbon credits [11,12]. Putting an experience into practice under this certification process is costly. Alternatively, it is different from other initiatives due to its funding source, which has a component to monitor and evaluate the installation of improved stoves [12].

In recent years, joint efforts have been made in order to coordinate activities and strengthen the value chain of improved stoves. The Government of Honduras (GoH), along with international cooperators, academics, and the private sector, has participated [6,7] in these efforts. Figure 3 shows the relationships of some stakeholders, as well as other agents, currently present in the delivery of improved stoves.

**Figure 3.** Stakeholder mapping of the clean cookstove sector in Honduras (modified from [9]).

Programs for the production, distribution and adoption of improved stoves in Honduras date back to the end of the last century; however, their greatest period of momentum has occurred in the current decade. International organizations—together with Honduran non-governmental organizations—initiated small scale programs during the past two decades [7,9]. These programs proved the advantages to health, forest conservation and energy efficiency when traditional stoves were replaced with improved ones.

The Honduran government joined these efforts in 2013, with a comprehensive manufacturing and distribution program, titled the *Better Life Program* [7]. Up to the end of 2017, around 600,000 improved stoves had been distributed throughout the country [7] (see Table 1). However, this number does not necessarily mean the stoves are currently being used, as not all people who received them have adopted the technology as of yet [13].


**Table 1.** Producers and improved stoves installed to December 2017 (Data from [7]).

There are only a small number of commercial suppliers that sell improved stoves in the country. Table 1 shows that the majority of suppliers are programs and Non-Governmental Organizations (NGOs). The GoH stands out with a 44% share in the production and distribution of stoves through the Envirofit and Fundeih (Envirofit Honduras and Fundeih are part of "Vida Mejor" Goverment Program. Envirofit build the stove and the Goverment pay to Fundeih, which distributes the stoves.) programs since 2013. The second largest program is Mirador project, an NGO that has been working in Honduras since 2004 and that has distributed about 180,000 improved stoves (equivalent to 29.3% of

the total). Additionally, Adehsa, Fundeih and Endev/Focae are also suppliers, with shares of 8.6%, 5.9% and 5.7%, respectively. Other smaller programs are also participating [7].

The goals and characteristics of these programs have not been homogeneous, although all are based on the benefits of replacing the traditional stove with an improved one. The main difference is whether objectives include the creation or expansion of the market for improved stoves. There are three market segments identified: (1) families in extreme poverty that are not able to pay for an improved stove and therefore require a total subsidy; (2) a second segment of limited economic capacity that requires a partial subsidy; (3) a third segment that operates in the free market of improved stoves.

Hence, for the first segment, programs should be aimed at those in extreme poverty; in such cases the improved stove would be donated. On the other hand, the Mirador project, although highly subsidized, also requires local inputs in terms of materials and labor [7]; this would be the case with the second segment. The program EnDev/Focaep seeks to create a market for improved stoves through paying attention to the different components of the value chain. In the same way, the Profogones project promotes a sustainable business model for improved stoves. The latter is linked to the *Vida Foundation*, with the Inter-American Development Bank (IDB) as the project administrator.

In practice, these programs could be considered complementary, due to the market segment they seek to fulfill. However, the way in which the government program is executed—i.e., with political objectives—distorts the rest of the market segments.

#### 2.1.2. Nationally Appropriate Mitigation Actions (NAMA)

Another effort to coordinate actions is Nationally Appropriate Mitigation Actions (NAMA), the objective of which is to increase the adoption of improved stoves in low-income households in Honduras. One of the main goals of NAMA is to bring improved stoves to 1.126.000 families by 2030 [10]. In the same way, NAMA will promote coordination and communication among stakeholders, generating comparable and transparent information, as well as the contributing to a common report of national advances in the reduction in greenhouse gases.

On the other hand, NAMA can also contribute to the strengthening of micro, small and medium enterprises that manufacture improved stoves and to the supply chain, due to the increased demand in the market.

Considering the need to unify and create synergies among multiple initiatives, the coordination of stakeholders and various programs of improved stoves will be one of the main challenges for NAMA and the National Strategy. Therefore, it is proposed that a *National Bureau of Improved Stoves*—that will benefit the coordination of the different stakeholders in NAMA—is established [10].

#### *2.2. Methodology and Data Used in the Cost-Benefit Analysis of a Strategy for Adoption of Improved Stoves in Honduras*

The methodology used to evaluate the cost-benefit of implementing a National Strategy for the adoption of improved stoves is based on using the LEAP (Software version: 2018.1.37, Stockholm Environment Institute. Somerville, MA, USA) software.

LEAP is an integrated, scenario-based modeling tool that can be used to track energy consumption, production, and resource extraction in all sectors of an economy. It can be used to account for both the energy sector and the non-energy sector, as well as greenhouse gas emission sources and sinks. In addition, LEAP can also be used to analyze emissions of local and regional air pollutants and short-lived climate pollutants, making it well-suited to studies of the climate co-benefits of local air pollution reduction [4,8].

LEAP is not a model of any particular energy system, but rather a tool that can be used to create models of different energy systems, in which each requires its own unique data structure. LEAP supports a wide range of modeling methodologies [6]. On the demand side, these range from bottom-up, end-use accounting techniques, to top-down macroeconomic modeling [8].

LEAP's modeling capabilities operate at two basic conceptual levels. At one level, LEAP's built-in calculations handle all the "non-controversial" energy, emissions and cost-benefit accounting calculations [8]. At the second level, users enter spreadsheet-like expressions that can be used to specify time-varying data or to create a wide variety of sophisticated multi-variable models, thus enabling econometric and simulation approaches to be embedded within LEAP's overall accounting framework [8].

In this study, LEAP is used for the calculation of the costs and benefits of implementing a strategy for the adoption of improved stoves in the urban residential sector (electrified and non-electrified), the rural sector and the commercial sector, with and without shares of Liquefied Petroleum Gas (LPG). The base year is 2016, and the target year is 2030. Variables were also established to be the most representative for the analysis of the energy sector: Population, GDP, income, households, GDP growth, population growth and demand growth.

According to the 2016 Honduras Energy Balance, the final energy consumption is 56.33% primary energy and 43.67% secondary. The final consumption of primary energy was divided into the main consumption sectors—residential, commercial and industrial. The share of each sector of primary energy consumption was determined as follows: the industrial sector with 13.17% energy consumption share, the commercial sector with 4.76% share, and the residential sector with 82.07% share. The latter value represents majority of the share.

The residential area was divided into urban and rural areas with shares of 54.1% and 45.9% of energy consumption, respectively. This energy consumption is driven by the factors of both rising household quantities and rising population.

Therefore, for both areas previously mentioned, the firewood consumption was taken. For the urban residential sector, 25% of households consume firewood, and for the rural residential sector, 77.96% consume firewood.

It is established that the traditional stoves account for an approximate 7.45 m<sup>3</sup> yearly consumption of wood per household, and the improved stoves accounts for only 2.13 m3 per household.

For secondary energy consumption in the residential sector, the sector was divided into urban and rural areas, and each of these areas was classified into electrified and non-electrified.

Electrified zones use mainly lighting, cooling, and cooking. In the cooking section, LPG was added, which represents 42% of the energy used for cooking; an average consumption value of 300 pound per year was assumed considering that a 25-pound container is consumed in each home per month.

On the other hand, by considering historic consumption, it is assumed that under reference scenario the LPG consumption per households will grow 18.4% per year.

For the non-electrified area, only the kerosene for lighting and the LPG for cooking are considered. In this scenario, only the LPG consumption for food cooking is analyzed, mainly in the peri-urban area of Tegucigalpa, the capital of Honduras. In this category, the use of LPG will rise to 36.8% in 2030. This is due to an assumed National Policy by the GoH, aimed to encourage the use of LPG due to the increasing electricity tariff. Finally, it is considered that there will be no increase in the use of LPG in rural areas.

#### 2.2.1. Scenarios

Three scenarios were used in the analysis, as follows:


By 2017, 583,295 improved stoves had been delivered, of which 20% have not been adopted by users (116,659 stoves). It is expected that by 2030, 1,125,000 improved stoves will have been already been installed, which implies that 658,364 improved stoves should be installed in that time.

#### 2.2.2. Manufacture Costs

The manufacturing costs of improved stoves are as follows:


These costs are introduced into the LEAP model, in such a way that they were annualized throughout the analysis period. Thus, the following figures (Figure 4, Figure 5, Figure 6) were obtained, which show the costs behavior from the base year up to 2030. It is assumed that a traditional stove has a cost of USD 34.00.

**Figure 6.** Annualized cost of improved stoves for the commercial sector.

On the other hand, the benefits of implementing a strategy for improved stove-adoption are broadly known:


Furthermore, before analyzing the cost-benefit of each scenario in comparison with the reference scenario, it is important to observe the energy consumption behavior of each scenario and contrast that behavior with the reference scenario, in order to have a better idea of what the implication of energy use in the cost-benefit analysis is.

Hence, the results of the energy consumption dynamics of each scenario are shown first. Then, the results of the cost-benefit analysis are presented.

#### **3. Strategy Implementation Results**

#### *3.1. BAU Scenario*

As mentioned earlier, in this scenario, the same considerations are being made under the same procedures throughout the study period. Figure 7 shows the household growth in Honduras up to 2030. This growth is 2.62% per year, according to official data.

Figure 8 shows that under the BAU scenario, energy consumption is constantly growing throughout the analysis. This figure only shows the primary energy consumption, which in this analysis considers solely firewood and bagasse. Bagasse is used in industrial demand, but this is not subject to the analysis for the implementation of an improved stoves strategy in energy demand, mainly for cooking food.

Figure 9 shows that the implementation of improved stoves in urban areas would follow a slow growth throughout the analysis period. Under this scenario, traditional stoves would be the main energy source needed for cooking food. Such stoves are based on burning firewood. The same behavior in energy consumption is shown in the rural area, as depicted in Figure 10. However, in rural areas, firewood consumption is higher.

**Figure 7.** Household growth (Thousands of households per year).

**Figure 8.** Total primary energy demanded under the BAU scenario (Thousands of Barrel of Oil Equivalent per year).

**Figure 9.** Firewood demand in urban households under the BAU scenario. (Thousands of Barrel of Oil Equivalent per year).

**Figure 10.** Firewood demand in rural households under the BAU scenario. (Thousands of Barrel of Oil Equivalent per year).

#### *3.2. Introduction of Improved Stoves vs. BAU Reference Scenario*

Under this scenario, the introduction of improved stoves in the Honduran energy sector is analyzed according to a National Strategy, whose goal is the installation and adoption of 1,125,000 improved stoves for cooking food.

Figure 11 shows that for the urban residential sector, the sharing of improved stoves implies a lower energy consumption throughout the analyzed period, in relation to the reference scenario (bars without color). In the same way, it is shown that traditional stoves should reduce their share at the end of the same period.

**Figure 11.** Firewood demand in the urban area according to the annual introduction of improved stoves until 2030. (Thousands of Barrel of Oil Equivalent per year).

Figure 12 shows that for rural areas, the energy avoided (bars without color) is less than for urban areas. However, the introduction of improved stoves decreases energy consumption throughout the analyzed period. This makes the sector more efficient in terms of the consumption of primary energy (firewood). It should be noted that when observing the scales in both figures, more wood is consumed in the rural area. The latter is verified by observing Figure 13, which shows the consumption of firewood for the urban and rural areas, considering both improved and traditional stoves.

**Figure 12.** Firewood demand in the rural residential area according to the annual introduction of improved stoves until 2030. (Thousands of Barrel of Oil Equivalent per year).

Figure 14 shows that if improved stoves are introduced in the commercial sector under this scenario, the consumption of firewood would be reduced throughout the analyzed period. For that reason, 22,000 barrels of oil (BEP) would be avoided—and that is only by 2030.

**Figure 14.** Firewood demand in commercial sector vs. what would be avoided according to BAU scenario.

#### *3.3. Introduction of Improved Stoves and LPG vs. BAU Scenario*

Figure 15 shows that under this scenario, LPG consumption increases throughout the analysis period. This observation is noticeable for the urban, electrified and non-electrified residential areas, as well as for the rural electrified households. These results are consistent with the fact LPG consumption will increase in the peri-urban areas of the urban sector.

**Figure 15.** LPG consumption for the stoves plus LPG scenario.

On the other hand, Figure 16 shows that in rural, non-electrified areas, it is expected that the consumption will be reduced even more. This due to the rise consumption of firewood.

Figures 17 and 18 show that more LPG is consumed under this scenario, both in the urban electrified and non-electrified areas. The label "all others" represent the years before 2021.

**Figure 16.** LPG consumption for the stoves plus LPG scenario. Rural residential area without access to electricity.

**Figure 17.** Comparison of the different scenarios in the LPG consumption for the stoves plus LPG scenario. Period 2021–2030. Electrified urban residential area.

**Figure 18.** Comparison of the different scenarios in the LPG Consumption for the stoves plus LPG scenario. Period 2021–2030. Urban residential area not electrified.

#### *3.4. Environmental Burden for the Di*ff*erent Scenarios*

The following figures show the emissions observed in the different scenarios. According to Figures 19 and 20, emissions resulting from a BAU reference scenario are greater than a scenario under

which a strategy of "Introduction of Improved Stoves" is implemented. On the other hand, under the scenario of LPG and improved stoves, emissions are higher (see Figure 21) than the emissions from the BAU scenario.

**Figure 20.** Emissions under the improved stoves scenario. (Thousands of Metric Tonnes).

**Figure 21.** Emissions under LPG and improved stoves scenario.

#### *3.5. Cost-Benefit of the Implementation of a Strategy for the Adoption of Improved Stoves in Honduras*

The LEAP tool shows that the improved stoves scenario is cheaper than the reference scenario (Table 2). This is concluded from the Net Present Value, which for the improved stoves scenario is USD 1253.8 million cheaper than the BAU scenario. For this reason, it would be cheaper to implement an improved stove-adoption strategy in the Honduran energy sector than to not. This conclusion includes the direct manufacturing costs of improved stoves as well as the costs of firewood for cooking.


**Table 2.** Cumulative Costs and Benefits of an Improved Stoves Strategy in Honduras: 2016–2030. Relative to Scenario: BAU. Discounted at 5.0% to year 2016. (Units: Million 2016 U.S. Dollar).

The cost of implementing such a strategy, considering the consumption of firewood (primary energy), is USD 844.3 million cheaper than the strategy's non-implementation.

On the other hand, the LPG plus improved stoves scenario shows a positive net present value of USD 376.7 million, so this scenario is more expensive than the reference scenario. The reason for this is that the share of LPG implies the import of a fuel that is not produced in the country.

Furthermore, the cost of avoiding emissions is lower in the scenario of improved stoves, at USD 496.7 per ton, in relation to the reference scenario. Hence, the implementation of an "Improved Stoves Strategy" in Honduras would reduce the emission of greenhouse gases more economically than the non-implementation of the strategy.

#### **4. Discussion: Towards a National Strategy for the Adoption of Improved Stoves**

Despite the existence of the structure showed in Section 2.1, strong leadership is necessary to achieve the objectives related to the support of the value chain in the process of adopting improved stoves.

Thus, the design and execution of a National Strategy for the adoption of improved cookstoves requires an institutional framework that considers not only the progress obtained so far, but also the challenges of the future. This requires leadership that actively promotes the different components of the strategy with a long-term vision. Therefore, such an integral policy should be implemented under the leadership of the GoH, given the need to coordinate efforts with different stakeholders.

Hence, among the different components for a National Strategy, the following must be included:

a. National Standard for Improved Stoves

When Honduras officially launched the standard of improved stoves OHN 97001.2017 [11], as part of the *PROFOGONES* project, the country became the third country in Latin America to establish the performance requirements to categorize improved stoves. The implementation of this standard promotes the dissemination of improved stoves for sustainable development in terms of health for users, reduction of pollutant emissions, an adequate use of natural resources, and economic benefits for users.

The OHN 97001:2017 standard establishes the minimum requirements of efficiency, safety, and quantity of intra-household emissions captured from an improved stove by categorizing models according to their performance.

b. Training Programs to Improve the use of Efficient Stoves and the Efficient Use of Firewood

One of the main goals of the National Strategy must be to make users aware of the benefits of using improved stoves. Training is important, as when the potential users are aware of the damages and ailments caused by smoke derived from the use of firewood, they will be able to better understand the need to change their method of cooking. This technological change implies strong behavioral changes regarding fuel, technology, and cooking; therefore, it is necessary to accompany users in this process, so that they do not abandon the technology in the face of difficulties [12].

#### c. Promotion of Financing Mechanisms

Evidence obtained during this study in Honduras shows that it is better to have an open market, stratify the target population who will be involved, know the material benefits, consider the subsidy according to the stratification of the participating population, and boost a market of pieces and parts of improved cookstoves. Evidence obtained during this study shows that it is better to have an open market, stratify the target population and subsidies, know the material benefits, and boost a market for the pieces and parts of improved cookstoves. Families unable to pay the total cost of an improved stove could be asked to cover a part of the cost working in the installation process. This participation improves the adoption of the new technology.

For the user who can pay, financing mechanisms must be created through local and/or regional credit institutions, i.e., rural savings banks, cooperatives, among others.

#### d. Monitoring and Evaluation

Currently, most programs that promote the establishment of improved stoves in Honduras are measured by the number of stoves built, distributed, and/or sold. However, this does not mean that the technology has been adopted and stoves are effectively being used. Few programs carry out monitoring and evaluation [14,15]. Therefore, in a National Strategy, it is important to broaden the approach of evaluating the process of building, distributing, selling and adopting stoves, to a methodology that includes the monitoring and evaluation of their use as well.

#### e. Certification and Applied Research

The certification will be used to evaluate the different types of stoves based on three characteristics established by the Honduran OHN 97001 standard for improved stoves [16]: (1) reduction in fuel use, (2) the capacity to reduce emissions, and (3) user safety. The foregoing will ensure that all stoves that are put into service meet the minimum standard criteria of fuel efficiency, indoor air quality, particles emissions and carbon monoxide, durability, and safety.

#### f. Stove Users and Producers' Associations

The main stockholders to consider will be users from low-income households in urban and rural areas that use firewood with traditional stoves. Women and children are the most exposed to air pollution inside the house. For this reason, female leaders must be trained in rural communities and neighborhoods in peri-urban areas as promoters responsible for coordinating demand and monitoring. Similarly, the training of master builders, i.e., builders of improved stoves, is needed.

#### **5. Conclusions and Policy Implications**

The cost-benefit analysis for the implementation of an Improved Stoves Strategy in Honduras was performed using the *Long-range Energy Alternatives Planning System* (LEAP) tool. The model shows the following results:



There are many stakeholders interested in the value chain of improved stoves in Honduras, a strategy for the adoption of this technology would have an impact on the process improvement and a reduction in direct costs and environmental externalities.

On the other hand, some lessons learned in the process of manufacturing and delivering improved stoves in Honduras could be the following:


Finally, the economic valuation of the external environmental benefits is difficult under this project. However, the authors believe this could be a good opportunity for future research in this important field of study.

**Author Contributions:** Conceptualization, W.C.F., B.B. and H.N.P.; methodology, W.C.F.; validation, W.C.F., B.B. and H.N.P.; formal analysis, W.C.F., B.B. and H.N.P.; investigation, W.C.F. and B.B.; resources, W.C.F., B.B. and H.N.P.; data curation, W.C.F.; writing—original draft preparation, W.C.F., H.N.P., S.R. and A.A.-S..; writing—review and editing, W.C.F., B.B., H.N.P., S.R. and A.A.-S.; visualization, W.C.F. and S.R.; funding acquisition, H.N.P., S.R. and A.A.-S. All authors have read and agreed to the published version of the manuscript.

**Funding:** Ministry of Foreign Affairs of The Netherlands and Khalifa University.

**Acknowledgments:** This study was possible through funding of the Ministry of Foreign Affairs of The Netherlands, through the Voice for Change Partnership program lead by SNV Netherlands Development Organization. Furthermore, it is supported by Khalifa University under Award FSU-2018-25.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Public Perceptions of Energy Scarcity and Support for New Energy Technologies: A Western U.S. Case Study**

#### **Alexandra Buylova 1,\*, Brent S. Steel <sup>1</sup> and Christopher A. Simon <sup>2</sup>**


Received: 2 December 2019; Accepted: 28 December 2019; Published: 3 January 2020

**Abstract:** This study examines public concern for energy security and support for public investment in new energy technologies. Using household survey data from the western U.S. states of California, Idaho, Oregon, and Washington, socio-demographic characteristics, environmental values, and policy relevant knowledge are analyzed as drivers of energy security and technology investment orientations. Findings suggest that a majority of respondents in each state believe that not enough money is being spent on energy research, that the country has insufficient energy resources, and that new technologies can support future energy security. Multivariate analyses indicate that some socio-demographic variables (e.g., gender and education), ideology, and environmental value orientations also have an impact on energy security orientations and support for technology investment.

**Keywords:** energy technology; energy security; public opinion

#### **1. Introduction**

This study contributes to the literature on determinants of public perceptions of new energy technologies and energy security by analyzing the impact of public energy-related knowledge, environmental value orientations, political ideology, and socio-demographic characteristics on public perceptions of energy security and new energy technologies. More specifically, using public opinion survey data from four Western states in the United States, we investigate public perceptions of: (1) the state of the country's energy supply; (2) being personally affected by the shortage of electricity in the next 5 years; (3) support for government investment into new energy technologies; and (4) the ability of new energy technologies to meet future energy demands.

In the process of policy formulation and implementation, one cannot ignore public opinion, especially in democratic societies like the United States [1–5]. Motivation to investigate public opinion toward energy-related issues and new energy technologies comes from the fact that the U.S. is a high energy consumer society with heavy reliance on fossil fuels in electricity generation and the transportation sector. Therefore, energy supply security is a central political and policy issue. At the same time, the country strives toward a low-carbon economy, diversifying its energy portfolio to include a larger share of renewable energy and other alternative energy technologies, including smart meters, electric vehicles, carbon capture, storage, and energy efficiency technologies [6]. Such policy innovations reflect the country's planning of energy independence and security, where renewables (i.e., wind, sun, biomass, nuclear) can be an alternative to traditional energy sources (e.g., coal, oil, and gas), which are finite in supply and are influenced by global fuel market price fluctuations [7–9]. A number of studies find strong support among the general public for renewable energy as a major source for future electricity portfolios [10–13]. In addition, the transition to low-carbon sources of energy satisfies environmental concerns and provides the added benefit of reduced marginal social costs, allowing the U.S. to respond to international diplomatic pressures of reducing CO2 emissions from burning

conventional fossil fuels. Despite a number of climate commitments, the U.S. remains one of the top emitters of greenhouse gases per capita [14,15].

Extant research on public support and opposition toward new energy technologies emphasizes the role of place, geographic proximity, land-use regulations, socio-economic impacts, fairness, and trust in shaping public opinion [2,16–18]. These studies investigate public perceptions of concrete energy projects, which are likely to carry specific drawbacks or opportunities for communities directly affected by new developments. On a more abstract level, other studies explore the general public's familiarity with new energy technologies, including wind energy [19], offshore renewable energy [20], smart meters [21,22], and electric vehicles [23]. These studies find that public opinion concerning energy technologies is often rooted in the degree to which those technologies are perceived as risky, with uncontrollable and catastrophic impacts [24], or tampering with natural processes [25–27].

However, to understand public opinion on broader energy policies in the era of low-carbon energy transition, there is a need to further analyze public orientations on energy-related questions. There is a lack of research that inquires into public perceptions of national and personal energy security issues, the level of government funding towards new energy technologies, and the ability of new technologies to meet energy demands of the future. This research addresses this gap in the literature. Moreover, we contribute to research on public opinion of energy policy and technologies by investigating the drivers of public perceptions that include environmental value orientations, political ideology, public knowledge, and socio-demographic characteristics.

#### *1.1. Environmental Value Orientations and Ideology*

Environmental values are commonly measured utilizing the New Ecological Paradigm (NEP) from Dunlap et al. [28]. The NEP scale consists of a range of ecological worldview aspects, such as a personal stance on humans' place in the ecosystem, the balance of nature, the rights of humans to modify the environment, and others. As expressed in the Values-Beliefs-Norms (VBN) model of environmental decision-making [29], values, or intuitive rather than calculative logic, can serve as reliable indicators of perceptions toward emergent clean energy technologies [30]. Extant literature finds support for the pro-NEP position as a significant indicator of positive attitudes toward new alternative energy sources [31] and government investments in alternative energy [32]. In this study, we investigate if and in what way environmental values shape public perceptions of new energy technologies, government investments in new energy technologies, and concerns regarding the security of energy supplies.

In addition to environmental values, Simon and Moltz [33] argue that political ideology and political party identification are significant moderators of public opinion about funding proposals in the areas of the natural environment, science, and alternative energy. In the area of climate change research, there are consistent findings that Democrats and more liberally-minded individuals are perceived to be more supportive of climate policies than Republicans and more conservatively-minded individuals [34–38]. Yet, there is a lack of investigation into the role of political ideology in shaping public opinion on energy security and alternative energy sources in the United States.

#### *1.2. Knowledge*

A review of the relevant literature demonstrates conflicting results about the role of policy relevant knowledge factors in influencing public opinion. Pierce et al. [39] found that more energy-informed citizens were more supportive of renewable energy policies. Hobman and Ashworth [31] discovered that a provision of additional information about a range of alternative energy technologies leads to greater public support for the use of said technologies. At the same time, Wolske et al. [27] contend that more information about carbon removal technologies may actually discourage public support, due to learning about new risks and potential impacts.

#### *1.3. Socio-Demographic Characteristics*

Steel et al. [32] show that younger and more educated respondents are more likely to support government policies related to clean energy technologies. Pierce and Steel [40] find that women and older individuals display a greater opposition towards alternative energy technologies. In regard to the public opinion on energy security, Knox-Hayes et al. [6] argue that women, less educated, and older individuals are more concerned over energy security. In this research, we investigate the following socio-demographic characteristics: age, gender, education, and income.

Our research objective is to understand how environmental value orientations, knowledge factors and socio-demographic characteristics are associated with concerns over energy security and public perceptions of new energy technologies in the U.S. context.

#### **2. Materials and Methods**

To address our research objectives, public opinion survey data were collected through household surveys conducted in California, Oregon, Washington, and Idaho in 2013. These states were selected because of their commitment to and investment in new clean energy technologies as part of their participation in the 2008 Pacific Coast Collaborative (PCC). The PCC is a regional approach to solving policy issues such as environmental protection and climate change, which has led the states to pursue aggressive renewable portfolio standards (RPS) and policies that encourage innovation in renewable energy technologies. In 2016, PCC states and the Canadian Province of British Columbia signed the 2016 Pacific Coast Climate Leadership Action Plan, which updated efforts at greenhouse gas emissions mitigation and adoption of community-scale renewable energy technologies. The state of Idaho is included as a control comparison. While it is also in the U.S. west and borders Oregon and Washington and is also heavily reliant on cheap energy from hydroelectric sources, it is more politically conservative and has not pursued state policies that promote the development and implementation of renewable energy technologies.

A mail survey with an additional link to an online option was sent to random samples of over 1400 households in each state. Even under the most strict sampling rules, assuming a 50/50 split in the population (i.e., 50% answer one way, while 50% answer the other way), to be 95% confident that an estimate from a sample survey is within +/− 3 percentage points of the true population value, a random sample of 1067 is needed for a population of 1 million and over [41]. Therefore, our sample size meets accepted standards of survey design. Samples were provided by a commercial research company that has exhaustive databases of households comprised of telephone directories, state departments of motor vehicle records, and other household information sources. Dillman's [41] Tailored Design Method was used in questionnaire design and implementation, which includes multiple reminder waves for non-responses and structured survey instruments and cover letters. A systematic sampling approach was applied within each household by asking those residents with the most recent birthday and over 18 years old to take the survey. Three waves of the mail questionnaires were distributed, followed by a final telephone reminder. Survey response rates vary only marginally across the four states, with the highest percentage in Oregon (51.5%), followed by 48.9% in Washington, 48.3% for California, and 46.6% for Idaho. Given the nature of the questions in the survey and the protections in place to protect individual respondent's identities, the Oregon State University Institutional Research Board determined that the research was "exempt" and therefore did not require full board review for ethical concerns.

In terms of survey response bias, we compared demographic data from the U.S. Census to survey data (Table 1). The Census data used is only for the section of the population that is 18 years and older as this aligns with the samples used. Survey respondents are slightly more affluent, older, and educated when compared to the Census data for each state. This finding is typical for survey research respondents [42]. The percentage of female and male respondents is almost identical to the Census data for all four states.


**Table 1.** Survey Response Bias.

<sup>1</sup> Data obtained from the U.S. 2010 American Community Survey.

#### **3. Results**

Measures related to the concern over energy supply, being personally affected by energy shortage, support for government investments into research and development of alternative energies, perceptions of new energy technologies, political ideology, environmental beliefs, knowledge about energy, and socio-demographic characteristics were formed from survey responses. The survey questions used to create variables are provided in Appendix A. See Appendix B for descriptive statistics for all measures.

To assess how informed the public is about energy policy, we asked respondents to report their level of familiarity with renewable energy policy. Response categories were oriented on a four-point scale ranging from 1 = "Not informed" to 4 = "Very well informed" (mean = 2.12). To assess respondents' knowledge about energy, we asked three energy-specific questions: (1) what is the largest source of energy for electricity in your state?; (2) what economic sector uses the greatest share of electricity in your state?, and (3) what does it mean to be "off-grid"? Answers to these questions were formed into a Quiz index ranging from 0 = no correct answers to 3 = three correct answers (mean = 1.09).

Ideology was measured on a five-point scale from liberal to conservative (1 = "Very liberal" to 5 = "Very conservative"; mean = 3.03). Environmental values were measured using the New Ecological Paradigm (NEP) six-item scale. Answers ranged from 6 = low level of support for NEP to 30 = high level of support for NEP (mean = 21.02; see Appendix A).

Demographic variables included the gender of the respondent (male vs. female; 51% female), age in years (mean = 49), income on a 10-point scale (1 = "less than \$10,000" to 10 = "\$200,000 or more"; mean = 5.32) and formal education attainment on an 8-point scale (1 = "less than high school" to 8 = "postgraduate degree"; mean = 5.17).

Descriptive statistics for questions about public perceptions of energy scarcity and electricity shortage reveal within sample and across state variation (Table 2). The difference between states is not statistically significant for the question about national energy resources (Chi-square = 11.094, *p* = 0.521), but is statistically significant for the question about concern over personal energy scarcity (Chi-square = 33.092, *p* = 0.001). The majority of respondents (over 50%) in all states agree or strongly agree that the country does not have a sufficient supply of energy resources. The largest percent of respondents who agree with this statement live in California, while the largest percent of people who disagree live in Idaho. Regarding the concern about being personally affected by electricity shortages in the next 5 years, there is significant variation across states. Yet, similarly to the previous question, respondents from California and Oregon express a higher level of concern compared to respondents from Idaho and Washington. Additional Chi-square tests comparing state by state separately revealed that for concern about being personally affected by a shortage of electricity, Californian respondents were significantly more concerned in each state-by-state comparison. Perhaps this is not surprising given the brownouts and power outages Californians have experienced over the past decade [40]. In addition, Idaho respondents were significantly different from each of the states, with fewer respondents being concerned about possible future power shortages. This may be attributable to the abundant, dependable, and low cost hydroelectricity available to most Idaho residents [40].


**Table 2.** Public perceptions of energy scarcity; variation across states.

Evaluating the descriptive statistics of public perceptions of new energy technologies, we observe that the responses are skewed toward agree and strongly agree answers for both statements: (1) that not enough money is being spent on research and development of alternative fuels and (2) that new technologies will make it possible to have enough electricity for all in the future (Table 3). Similar to the findings about perceptions of energy scarcity, a larger percentage of respondents from California and Oregon expressed concern over the level of funding for research and development. Also, a larger proportion of respondents from California and Oregon believed in the future potential of new energy technologies, compared to respondents from Idaho and Washington. In both cases the difference between states is statistically significant (Chi-square = 23.466, *p* = 0.024 and Chi-square = 21.925, *p* = 0.038, respectively).


**Table 3.** Public perceptions of new energy technologies; variation across states.

As with the analyses presented in Table 2, additional Chi-square tests were conducted for state-to-state comparisons. Concerning the statement that not enough money is being spent on research and development, California and Oregon respondents were not significantly different in their responses, and the same can be said with Idaho and Washington respondents. However, the Chi-square analyses showed that California and Oregon respondents were significantly different from Idaho and Washington survey participants in their level of agreement and disagreement with the statement. California and Oregon respondents were slightly less like to disagree with the statement and more likely to agree.

For the final statement in Table 2, concerning new technologies contributing to electricity for all in the future, the additional Chi-square results show that California respondents were significantly different from the other three states in their agreement with the statement. While over 70 percent of respondents in each state agreed or strongly agreed with the statement, Californians were significantly less likely to disagree with the statement and more likely to agree with the statement when compared to each other state separately.

Due to skewed distribution of dependent variables, measures were recoded into binary variables (1 = agree, 0 = else) and a logistic regression analysis was performed to estimate the relationships between dependent and explanatory variables. Table 4 highlights results of the logistic regression output for two dependent variables: concern over energy scarcity and concern over personal energy shortage. Among socio-demographic factors, the findings indicate that being female and having a higher level of formal education is significantly associated with a lower level of concern over energy scarcity, while higher income is significantly associated with a lower level of personal concern over energy shortage. For the knowledge variables, respondents who are more familiar with renewable energy policy are less likely to be concerned over energy scarcity, while those with a better performance on an energy quiz have lower levels of concern over personal energy shortage. Among value and ideology factors, a higher score on the New Ecological Paradigm scale is associated with greater concerns about U.S. energy security, as well as personal energy security. Finally, being more politically conservative has shown to be associated with greater concern over personal energy security.



a.1 = Agree that country does not have enough energy resources, 0 = else.b.1 = Agree will be personally affected by energy shortage, 0 = else. NEP = New Ecological Paradigm.

Table 5 presents results of the logistic regression for the second set of dependent variables on energy technology beliefs. Here, we discover diverging results regarding the influence of gender. Females express greater concern that not enough money is spent on research and development of technologies. At the same time, they are less likely to think that technologies will provide energy for all in the future. Respondents with more advanced formal education are less likely to believe in the impact of technology on future energy supply, while higher income level is associated with lower level of concern that not enough resources are being spent on research and development. Concerning the impact of environmental values, those respondents with higher NEP scores are more likely to agree that not enough money is being spent on research and development. Finally, being politically conservative is associated with lower levels of concerns over the shortage of funding for research and development of new energy technologies and lower levels of perception that technologies will supply energy for all in the future.


**Table 5.** Logistic regression estimates for energy technology beliefs.

<sup>a</sup> 1 = Agree that not enough money spent on research and development of alternative fuels, 0 = else. <sup>b</sup> 1 = Agree new technologies will make it possible to have electricity for all in the future, 0 = else.

#### **4. Discussion**

#### *4.1. Environmental Value Orientations and Ideology*

Reflecting results of previous studies that show a connection between environmental value orientations and pro-environmental behaviors, such as displaying positive attitudes for new alternative energy sources [31] and government investments in alternative energy [32], this study finds that pro-environmental values are associated with public perceptions that not enough resources are being devoted to research and development of new energy technologies and greater concerns about the U.S. energy security, as well as personal energy security. At the same time, these respondents do not seem to support the idea that new technologies can ensure energy supply for all in the future. It is possible that respondents with higher biocentric scores on the NEP scale are concerned about the potential negative impacts of new technologies on the environment [24–27], and thus, the extent to which technologies should serve as a solution to energy problems in the future. Building on the research by Simon and Moltz [33], who contend that political ideology is a strong predictor of public opinion about government spending in areas of environment and technologies, we demonstrate that conservatives are less concerned about the insufficiency of government funding towards research and development of new energy technologies, and are also less likely to believe that alternative energy technologies are capable of being an adequate energy resource in the future. Government investment in new energy technologies implies a number of politically sensitive issues concerning the role of government involvement in the energy market and growth of renewable energy market share. Our

findings suggest that conservative leaning respondents are reluctant to provide government support for new energy technologies [40]. At the same time, conservatives also displayed higher concern about experiencing personal energy shortages.

#### *4.2. Knowledge*

Similar to prior studies on the connection between knowledge and public opinion about new technologies [27,31,40], we found that greater familiarity with renewable energy policy is associated with lower concerns over the country's energy scarcity. It is possible that respondents who are more familiar with renewable energy policy have a better understanding of energy policy in general and, therefore, are confident in the ability of the market and the government to ensure a reliable energy supply in the future, regardless of the type of energy technologies employed to accomplish that. As we show, trust in new energy technology's ability to provide energy supply for all in the future is not associated with renewable energy policy familiarity. In regard to the energy knowledge quiz, respondents who scored higher on the quiz, have fewer concerns about being personally affected by the electricity shortage in the next 5 years. It is worth mentioning that questions on the quiz were state-specific. Therefore, our findings showcase an idea that being informed about local energy issues is associated with lower levels of concern about being personally affected by shortages of energy supply. Interestingly, neither familiarity with renewable energy policy nor energy knowledge variables are associated with perception of the level of government funding of new technologies or the power of new technologies to ensure a sustainable supply of energy in the future. This discovery suggests a diversion from previous research findings on the connection between knowledge and public opinion about new technologies [27,31,40]. We establish that familiarity with general energy issues and renewable energy policy is not necessarily associated with public perceptions on government investments into new energy technologies or on the technical capabilities of those technologies.

#### *4.3. Socio-Demographic Characteristics*

Contrary to findings by Knox-Hayes et al. [6], we discover that women are less concerned about energy security issues when compared to men. This is an interesting finding, because a number of studies in sociology and psychology demonstrate systematic differences between men and women in attitudes toward risk, arguing that on average women tend to be more risk averse [43]. Thus, in our work, we would expect women to be more concerned about the energy security issue than men. However, as Eckel and Grossman [43] contend, when looking at gender attitudes toward risk, it is important to account for other demographic factors such as knowledge, wealth, marital status and others. It is possible that in our study women are less concerned about energy security issues because our sample is slightly more affluent and with higher level of education than the population. At the same time, women are also more likely to perceive a shortage of government funding towards research and development of new technologies. Attesting to the connection among the demographic factors, we find that more educated respondents are less likely to be concerned over energy security in the future, personally and for the nation as a whole. It is possible that respondents with a higher level of education enjoy higher incomes, and therefore, a greater sense of personal security over any future event. To support this statement, we show that those with higher incomes are less concerned about being personally impacted by electricity shortage in the next 5 years. Furthermore, respondents with higher levels of formal education are less likely to believe in the power of new technologies to support a reliable supply of energy in the future. It is possible that the more educated public accepts a more cautious view about the successful and rapid integration of new technologies into the market. As we observe, respondents leaning toward conservative political views also take on a more reserved stance about the feasibility of new energy technologies securing a sustainable supply of energy in the future. Finally, age did not play a role across any of the analyzed opinions. This is an interesting finding, as we may expect that the respondents belonging to the generation that lived through the oil crisis of the 1970s, a period infamous for oil shortages and high energy prices [44], would be more concerned

about energy shortages in the future. At the same time, we may also assume that a younger generation would be leaning toward higher trust of new energy technologies.

**Author Contributions:** Contributions of the individual authors in this work are the following: conceptualization, B.S.S. and C.A.S.; methodology, B.S.S.; formal analysis, B.S.S. and A.B.; resources, B.S.S. and C.A.S.; data curation, B.S.S.; writing—original draft preparation, A.B.; writing—review and editing, A.B., B.S.S., and C.A.S.; visualization, B.S.S. and A.B.; supervision, B.S.S.; project administration, B.S.S.; funding acquisition, B.S.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Oregon Policy Analysis Laboratory, School of Public Policy, Oregon State University, and the United State Department of Agriculture grant "Climate Change Adaptation, Sustainable Energy Development and Comparative Agricultural and Rural Policy," National Institute of Food and Agriculture (NIFA) Higher Education Challenge (HEC) Grants Program.

**Acknowledgments:** We would like to thank former master students in the School of Public Policy at Oregon State University: Mariana Amorim, Courtney Flathers, Andrew Spaeth, Lyndsay Trant, and Iaroslav Vugniavyi for their contributions to the data collection.

**Conflicts of Interest:** The authors have declared that no competing interests exist.

#### **Appendix A**


We now have a few concluding questions to check if our survey is representative of all types of people. Please remember that all answers are completely confidential to the extent permitted by law.



Which category best describes your household income (before taxes) in 2014?


#### **Knowledge Questions**

Familiarity:

In general, how well informed would you consider yourself to be concerning renewable energy policy issues in (state)—such as wind, solar, wave, and biomass energy?

#### 1. Not informed


#### Energy Quiz:

Here are a few specific questions about energy. Many people don't know the answers to these questions, so if there are some you don't know just leave them blank and continue.


The Quiz variable is an additive index of correct answers. Correct answers are: (a) Idaho, Oregon and Washington–hydroelectric; California–natural gas; (b) California, Oregon and Washington–transportation; Idaho–industrial; (c) Producing one's own electricity.


Statements 2, 4 and 6 above were recoded to: 5 = biocentric response and 1 = anthropocentric response. The items were then used in an additive index that ranges from 6 to 30. Chronbach's alpha is 0.759.


#### **Appendix B**



#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **A Conceptual Framework to Understand Households' Energy Consumption**

#### **Véronique Vasseur 1,\*, Anne-Francoise Marique <sup>2</sup> and Vladimir Udalov <sup>3</sup>**


Received: 5 October 2019; Accepted: 4 November 2019; Published: 7 November 2019

**Abstract:** Households' energy consumption has received a lot of attention in debates on urban sustainability and housing policy due to its possible consequences for climate change. In Europe, the residential sector accounts for roughly one third of the energy consumption and is responsible for 16% of total CO2 emissions. Households have been progressively highlighted as the main actor that can play a substantial in the reduction of this energy use. Their behavior is a complex and hard to change process that combines numerous determinants. These determinants have already been extensively studied in the literature from a variety of thematic domains (psychology, sociology, economics, and engineering), however, each approach is limited by its own assumptions and often omit important energy behavioral components. Therefore, energy behavior studies require an integration of disciplines through interdisciplinary approaches. Based on that knowledge, this paper introduces a conceptual framework to capture and understand households' energy consumption. The paper aims at connecting objective (physical and technical) with subjective (human) aspects related to energy use of households. This combination provide the answers to the 'what', the 'how' and most importantly the 'why' questions about people's behavior regarding energy use. It allows clarifying the numerous internal and external factors that act as key determinants, as well as the need to take into account their interactions. By doing so, we conclude the paper by discussing the value of the conceptual framework along with valuable insights for researchers, practitioners and policymakers.

**Keywords:** households; energy consumption; pro-environmental behavior; conceptual framework

#### **1. Introduction**

Households' energy consumption has received a lot of attention in debates on urban sustainability and housing policy due to its possible consequences for climate change [1–3]. The residential sector is responsible for 17% of global CO2 emissions in the world and constitutes the third-largest major energy consumer worldwide [3]. According to Brounen et al. [1], about 20% of total global energy demand originates from the requirements to heat, cool, and light residential dwellings. In Europe, the residential sector stands for roughly 30% of the energy consumption and is responsible for 16% of total CO2 emissions. According to the Environmental Investigation Agency (EIA) [2], households in Europe accounted for 21% of the world's total residential energy consumption in 2012. Space heating is responsible for the most important part of energy used by households. In accordance with recent literature highlighting the strong relationships between building energy consumption, location, transportation and urban form [4–7], individual mobility is considered in this paper as part of the

energy uses at the household level. Transportation indeed represents a significate part of households' energy consumption [8,9]. Last but not least, transportation in the sole sector, at the European level, in which energy consumption and related emissions of greenhouse gases is increasing.

Reducing domestic energy uses is necessary, especially to achieve the international and national commitments to significantly reduce carbon emissions. By 2050, the European Union should cut greenhouse gas (GHG) emissions to 80% below 1990 levels. The milestones to be achieved are 40% cuts by 2030 and 60% by 2040 [10]. Still according to this low-carbon economy roadmap [10], emissions from the building sector (houses and offices) could be cut by around 90% in 2050 by improving drastically three strategies: passive housing technologies for new building; refurbishing old buildings and substituting electricity and renewables for fossil fuels in heating, cooling and cooking. In this search for more energy efficiency in the domestic sector, three main strategies have been the focus on extensive review in the current literature and are namely summarized within the "Trias Energetica concept" developed by TU Delft [11], consisting in three consecutive steps:


This framework, as well as recent research [5,12,13] put the focus on the crucial need to reduce energy demand as the first and most efficient way toward a sustainable future. As far as regulations and policies are concerned, there are numerous local, national and also international regulations and policies aiming to reduce energy demand by strict technical requirements. For buildings characteristics, the European directive on the energy performance of buildings came into force in 2002, and was progressively strengthen to impose, by 2018 for public buildings and by 2020 for all new buildings to be nearly zero energy buildings. Retrofitting the existing building stock has also been highlighted as the main target to achieve [10], especially in Europe where the renewal rate of buildings is low [14–17]. Regulations on maximum CO2 emissions for private vehicles are also periodically strengthen whereas initiatives focused on changes in consumption patterns, and the use of energy in a greener way remain more limited.

In energy efficiency research, households have been progressively highlighted as the main actor that can play a substantial in the reduction of this energy use [18–20]. Households' energy consumption is a complex and hard to change process that combines numerous determinants. It is made up by different characteristics of the building and the neighborhood in which the household live, by the energy-using appliances and heating/cooling systems, but more importantly by a variety of internal and external factors, such as households' beliefs, values and attitudes, other people's behaviors, and various economic incentives.

For example, Jones et al. [21], based on Wei et al. [22] and a review of the literature summarized key determinants (here for space heating) into four main categories, as follows:


Each determinant considered alone, or some combinations, of determinants within the same category, has already been extensively studied in the literature and research on household energy consumption has mainly focused on the economic and technological aspects of this issue, while most of the policy action has aimed at reducing information barriers and providing financial incentives (see the literature overview for overview of the key literature that identify the factors affecting housholds' energy consumption). In this perspective dominated by neoclassical economics, a growing body of

research in behavioral sciences and sociology showing that household energy consumption is far more complex than the assumptions made in cost-benefit analyses has largely been overlooked. Actually, it is formed by a combination of factors, not only individual factors but also contextual factors are of importance. Due to this complexity, household energy consumption is often studied using a more fragmented and disciplinary studies from a variety of thematic domains such as psychology, sociology, economics and engineering. While technological approaches focus on quantifying energy consumption as a support for decision-making, approaches in the social sciences focus on understanding and explaining actual energy behavior. Nonetheless, each approach is constrained by its own assumptions and it often omit important energy behavioral components. Therefore, energy behavior studies require an integration of different disciplines by using an interdisciplinary approach.

In this context, the aim of this paper is to introduce a new conceptual framework to capture and understand the households' energy consumption. The paper aims at connecting objective (physical and technical) with subjective (human) aspects related to energy use by households. This combination aims at providing the answers to the 'what', the 'how' and most importantly the 'why' questions about people's behavior regarding energy use. In order to underhand how households' energy consumption work, Section 2 firstly provides the methodology followed by review of exiting behavioral change theories analyzing and identifying strengths and weaknesses of the models (Section 3). Such analysis combines technical and behavioral determinants of energy consumption as well as environmental influence constituting a set of aspects which leads to develop a differentiation of the main aspects of households' energy consumption. Then, Section 4 proposes a new comprehensive conceptual framework concerning determinants of the external and internal context. Finally, Section 5 summarizes our main findings and highlights new insights and perspective for future research in households' behavior and energy efficiency.

#### **2. Methodology**

The first part of this research is a literature review in order to define more clearly what is to be examined, with the intention of having a sufficient outline for determining what data to collect and how to analyse the data in practice [23]. The literature review consists of 4 steps: (1) selection of papers; (2) preliminary analysis; (3) detailed analysis; and (4) framework development.

#### *Step 1: Selection of papers*

The literature were searched on Scopus and Web of Science online databases due to their ability to allow fast and customized searches. The basic terms for the review were identified as "energy efficiency" and "behavior", the first search on the database was performed using the "energy eff\*" which included both "energy efficiency" and "energy efficient". Next, the search was limited to journal articles in English only. A further filtering based on title reviewing was carried out and we determined the articles relevant enough to be included in the analysis. The criteria used for the inclusion of the articles were the following:


The literature review included a broad range of scientific literature: action determination models; environmental behavior models; the social practices approach. This search of literature resulted in a total of more than 150 peer-reviewed studies.

#### *Step 2: Preliminary analysis*

We have grouped the papers according to different main lines: terminology; pro-environmental behavior models; and drivers and barriers. In doing so, this review aims also to complement and update previous reviews on households' energy consumption and other pro-environmental behavior models.

#### *Step 3: Detailed analysis*

A detailed analysis on both categories of energy reductions in households: the technical and behavioral energy saving measures is carried out. Followed by an overview of the most influential and commonly cited behavioral models or frameworks developed in socio-psychological research in order to provide a comprehensive explanation of energy consumption of households are described in detail, including the strengths and the weaknesses. The research topic of drivers and barriers has gained a lot of the attention of the academic community, as understanding the nature of these drivers and barriers is essential for the success of energy related policies that might encourage efficiency investments of households.

#### *Step 4: Framework development*

Based on this overview (step 3) it became clear that little research is available on what individual and social factors might influence the adoption of novel energy consumption and investment practices in households' and there is a stringent need to understand the barriers to and drivers of involvement in these. These insights and guidelines were used as a basis to build our conceptual framework on how to improve our understanding and knowledge of households' energy consumption. The framework should provide a deeper understanding in the 'what' (what factors are associated with households energy consumption, e.g., financial costs or visibility), the 'how' (how can these factors be influences, e.g., technical solutions or public policy initiatives) and the 'why' (why different types of households' are likely to behave in different context e.g., certain choices can be explained by income) in order to promote and sustain conserving practice.

#### **3. Literature Review**

#### *3.1. Energy E*ffi*ciency in Households: Key Definitions*

Various terminologies are used in the literature to describe the reduction of energy use in households. Many terms start with "energy", (energy savings, energy conservation, energy consumption, energy efficiency), while others stress more the attention on "behavior" (efficiency behavior, energy usage behavior, curtailment behavior, energy related behavior) or on the "measures" (energy saving measures, technical energy saving measures, energy efficiency measures, energy conservations measures, behavior energy saving measures) [24–27].

In order to reduce energy use in households, two broad categories of actions can be identified: "once-off actions" to save energy and "ongoing day-to-day actions" to reduce energy consumption. Once-off actions are related to efficiency behavior realized through technical energy saving measures (or energy efficiency measures). Less energy is used for a constant service, for example, an older equipment (washing machine, vehicle, etc.) replaced by a more energy efficient model (energy-efficient appliances) or investing in home improvements like insulating the roof or replacing the glazing but more efficient one. These technical measures can significantly reduce households' building and transportation energy uses and save energy and costs over long periods of time. However, they are seen as an expensive way to reduce energy consumption as they often require an initial investment. In this debate, it is also worth mentioning that, despite a growing trend to energy vulnerability of some low-income households, in Europe, energy prices (for gas, coal but also fuel for vehicles) remain relatively cheap [28,29], which led to longer return on investment for hard works such as insulation. The shift from fossil fuel to renewable energy needed to complete the international targets on CO2 emissions should however lead to an increase in energy prices to finance this shift [30].

Day-to-day actions refer to the reduction of energy consumption through using less of an energy service as part of people's lifestyles. Turning the thermostat down a degree or two in the wintertime

for example, switching off the lights, or modal shift from car to bike for short trips, etc. However, these measures are often associated with additional effort or a decrease in comfort. These behavior energy saving measures or energy conservations measures refer curtailment (energy conservation) behavior. Table 1 summarizes the main characteristics of the two previously highlighted categories of actions toward households' energy consumption.



Researchers have not been able to quantify whether efficiency behavior or curtailment behavior is more effective [24]. Some researchers have argued that curtailment behaviors initiate actual behavioral changes and sustain them for long-term [31], while others has suggested that efficiency behavior is in fact generally more effective in obtaining actual energy savings [24]. The success of the latter (efficiency behavior) may be counteracted by the "rebound-effect" (reduction in expected gains from new technologies that increase the efficiency of resource use through behavioral responses) [32].

Considering these aspects, this paper considers both categories of energy reductions in households: the technical and behavioral energy saving measures, the latter seeming somewhat overrepresented even with the knowledge that the energy saving potential of the technical measures is considered equal. The interplay between macro-level (e.g., technological innovations) and micro-level factors (e.g., use of technological innovations) will be studied in detail.

#### *3.2. Theoretical Framing*

Several behavioral models have been developed in socio-psychological research in order to provide a comprehensive explanation of energy consumption of households. The most influential and commonly cited frameworks are described in this section.

#### 3.2.1. Action Determination Models

Many approaches could be categorized under the generic term of action models or action determination models. One of them is the Theory of Planned Behavior (TPB), a classical framework that has proven to be successful in explaining behavior intention and attitude in the field of household energy consumption. The TPB developed by Ajzen [33] proposes that behavior is preceded by the formation of behavioral intention. This behavioral intention depends on attitudes towards the behavior, social norms, and perceived behavioral control (the belief on whether one is capable of performing the behavior). TPB suggests that, for a specific behavior, the more active Behavioral Intention (BI) is, the more intense Subjective Norms (SN) and feel the less difficulties, individuals will be more likely to implement this behavior.

Behavioral research suggests that, values are the basis of attitude formation and it could predict behavior in a more stable and durable way than attitude [33]. In the field of environmental behavior, the Value-Belief-Norm (VBN) theory proposed by Stern et al. [34] and Stern [35] is the classical theory to study how environmental values affect the behavior. Stern divided environmental values into three dimensions: self-interest values (SV) is the belief that environmental problems will affect self-interest; altruism values (AV) is the belief that environmental issue affect others and long-term interest; biosphere values (BV) focus on natural environment intrinsic values, suggest human could not destroy the nature. The theory of VBN suggests that, environmental values are the primary antecedents to inspire public responsibility consciousness and further implement eco-environmental behavior. Another similar framework in the same line of research is the Norm-Activation Model (NAM) [36,37]. Both theories (VBN and NAM) are rooted in the thought that energy is conserved when people feel a moral obligation to do so. The VBN-theory further assumes that awareness of the problems is rooted in environmental concern and values. Thus for explaining low-cost energy curtailment behaviors, the NAM and VBN theory appeared to be successful.

However, the explanation of pro-environmental behavior is incomplete if only internal factors are considered. Guagnano, Stern et al. [38] suggest the ABC model, which incorporates the relationships of contextual factors (C), attitudes (A) and behavior (B). The ABC model involves the strategies for integrating internal processes and external conditions. Behavior is formed through the combination of personal attitudinal variables and contextual factors. Attitudinal variables include internal factors such as specific attitudes, beliefs, norms, values, information and a tendency to act in certain ways, whereas contextual factors include external factors such as physical capabilities and constraints, social institutions, legal factors and economic forces like monetary incentives and costs. The ABC model postulates that the corresponding behavior is associated with both attitudes and external conditions suggesting that behavior is an interactive product of personal-sphere attitudinal variables and contextual factors [35].

#### 3.2.2. Social Practice Theory

Social practice theory (SPT)refers to "a routinized type of behavior which consists of several elements interconnected to one other: forms of bodily activities, forms of mental activities, 'things' and their use, a background knowledge in the form of understanding, know-how, states of emotion and motivational knowledge" [39]. It is increasingly being applied to the analysis of human behavior, particularly in the context of energy consumption. Nowadays, this theory is used as an umbrella approach under which various aspects of theory are pursued rather than a single (or specific) theory. Here the work of Shove (Lancaster University) on consumption and the group around Spaargaren (University Wageningen) on change processes is of particular relevance. The primary insights focusses not on individual behavior but on social practice and on the interaction of people's practices and in particular their material contexts. This leads towards reflecting upon why certain practices are done, and how and why other practices are prevented. Shove stresses the importance on how social practice have changed over time, how it becomes normal and what the consequences on sustainability are. She is doing this using the concepts of cleanliness, comfort and convenience [40,41]. Spaargaren uses Shove's theoretical approach and place the social practices into a conceptual model, which has a strong emphasis on sustainability of existing lifestyles and on the ecological modernization of the society [42].

#### 3.2.3. Integrated Perspectives

Nowadays, the conducted studies seem to focus more on the interaction of multiple factors, the integrating of different theories/perspectives and the multiplicity of forces underpinning energy consumption and conservations. Venkatesh [43] proposed the Unified Theory of Acceptance and Use of Technology (UTAUT), a synthesis of eight existing models of technology acceptance. The model integrates elements from Theory of Reasoned Action (TRA), Motivational Model (MM), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), a combined Theory of Planned Behavior/Technology Acceptance Model (C-TPB-TAM), Model of PC Utilization (MPCU), Innovation Diffusion Theory (IDT), and Social Cognition Theory (SCT).

Turaga et al. [44] integrated for example the moral considerations of VBN with the rational framework of TPB and Bamberg [45] combined the TPB and the NAM. Abrahamse et al. [24] proposed that both micro-level factors and macro-level factors can influence household energy consumption. And, some researchers have investigated different types of energy consumer profiles in order to pinpoint what specific factors are associated with energy-saving behavior, e.g., Guerra Santin [46] and Gaspar and Antunes [47].

Table 2 summarizes the main behavior change models used in energy research. The name of the model and its principal proponent is given in Table 2, followed by some strengths and weaknesses. Due to the restricted length of this paper, it will not have been possible to describe every single facet of each model.


**Table 2.** Strengths and weaknesses of behavior change models.


**Table 2.** *Cont.*


**Table 2.** *Cont.*

For some behavior change models we do not have indicated any weaknesses or relevant empirical evidence (see empty cells in the table).

The diversity and variety of the behavioral models or theories have shown that pinpointing the right type of constructs/indicators to achieve behavioral change is not straightforward. Jackson ([49]) sums up this problem in his discussion of consumer behavior. *"Beyond a certain degree of complexity, it becomes virtually impossible to establish meaningful correlations between variables or to identify causal influences on choice. Conversely,... simpler models run the risk of missing out key causal influences on a decision, by virtue of their simplicity... this means that there will always be something of tension between simplicity and*

*complexity in modelling consumer behavior. More complex models may aid conceptual understanding but be poorly structured for empirical quantification of attitudes or intentions (for example). Less complex models may aid in empirical quantification but hinder conceptual understanding by omitting key variables or relationships between key variables".*

Behavior is a complex combination of different constructs/indicators (values, norms, habits, social factors) and changing any of these can be challenging. Last but not least, it is worth mentioning that there is not a framework that is universally accepted by scholars as providing a comprehensive explanation of households' energy consumption and conservation.

Although the overview provided in this paper does not intend to be exhaustive and the selected models vary in purpose and context, the following insights and guidelines can be highlighted and used as a basis to build our conceptual framework:


#### *3.3. Households' Energy Consumption: Key Determinants*

Much research has been conducted over the years to clarify the key determinants that influence households' energy consumption. They may have been differing motives as to why research has looked at this domain, however, the overarching aim has been the focus on the reduction of energy consumption. Whether it is considered from an economic perspective (household's energy consumption linked to and have monetary impacts) or from a perspective related to environmental impacts does not matter. This section provides an overview of recent developments in the literature with regard to factors influencing households' energy consumption. Non-residential buildings are out of the scope of this paper and therefore literature in this field is not considered.

A classification of the identified influencing factors underlying this behavior in the residential sector is proposed to identify the determinants affecting households' energy consumption. Gärling et al. [76] argued that in order to change people's environmental behavior there is a need to consider both macro and micro-level factors. Jackson [49] divided all the influencing factors into internal factors (including attitudes, beliefs and norms) and external factors (including regulations and institutions). This paper follows his classification line and examines the following classification for the factors as possibly affecting energy-saving behavior: internal level factors, external level factors and social factors. Regarding the latter, previous research [41,49] has come up with useful conclusions that the social embeddedness/ social context is understudied. More in detail, how individual choices are continually being shaped and reshaped by the social contexts is important to consider in this research.

#### 3.3.1. Internal Level Factors Influencing Household Energy Consumption

Various internal level factors influence household energy use and energy savings. Steg and Vlek [77], one of the most relevant publications on residential energy behaviors, identified motivational factors, contextual factors and habitual behavior as the most important factors in environmental behavior.

*Motivational factors* are defined as subjective individual characteristics that may influence how people perceive and rate the acceptability of objective characteristics of energy alternatives. *Habitual*

*factors* refer to individual factors habitual and guided by automated cognitive processes rather than being preceded by elaborated reasoning. How these habits are formed, reinforced and sustained is important for designing effective interventions to modify this behavior. *Contextual factors* refers to the objective characteristics of energy alternatives determined by its own context for example the energy price.

#### 3.3.2. External Level Factors Influencing Household Energy Consumption

The second group of identified influencing factors place behavior as a function of processes and characteristics external to the individual, these include amongst other fiscal and regulatory incentives, and institutional constraints. In the literature, the term 'institution´ can play different roles in transition trajectories/ innovations and various authors [78–80] do not mean the same things when using this term. More in detail, Lundvall consider institutions as 'things that pattern behavior' (such as norms, rules, and laws), while Nelson and Rosenberg institutions consider as 'formal structures with an explicit purpose' (often called as organizations) [79]. In this research, the term institutional factors are used to describe the rules, regulations, standards and so on that shapes the behavior of households in terms of perceptions and actions. Institutional change can therefore greatly influence how households perceive and respond to uncertainties in the energy usage.

#### 3.3.3. Social Factors Influencing Household Energy Consumption

The effect of social interaction on energy-saving behavior is also emphasized in some studies [81–86]. Social norm and social identity studies in the energy domain have generally looked at their influence on consumption patterns and have showed their effectiveness when used in intervention studies to reduce energy consumption. As social norms signal what the members of the communities we live in do, as well as what they approve or disapprove off, they are an important determinant of individual behavior both at home and on the road.

Furthermore, the importance of considering the group membership as an indicator of the importance of cultural contexts and social influences on consumer behavior has also been identified in previous research [87–90]. Individuals with a strong sense of group membership (i.e., with a high group identification), typically express positive evaluations, display the tendency to act in favor, and strive to maintain a positive image of their in group, even at the expense of an out-group. Social psychological studies showed social identity as one of the main psychological factors leading to voluntary cooperation to solve commons problems or dilemmas by postponing their narrow self-interest and to act on behalf of their group, community or place.

Table 3 gives an overview of the most commonly identified influencing factors correlated with household energy consumption, both energy efficiency and energy conservation.


**Table 3.** Overview of the key literature that identify the factors affecting households' energy consumption.

The concepts of "day-to-day actions" and "once-off actions" presented in Chapter 2 are particularly used in this vision. Household's energy saving behavior indeed includes a wide range activities from habitual day-to-day actions to sophisticated and costly once-off actions [27]. That is why it should be

noted that the above determinants of household's energy conservation behavior affect these various types of activities in different ways depending on type of behavior and involvement with the product and behavior and have different psychological properties [99].

While once-off actions are one-time purchase decisions characterized though initial financial expenses and the potential for future savings, curtailment behaviors or ongoing day-to-day actions are considered to be routinized or habitual in the sense that it spares individual's time and effort of decision-making on issues that re-occur regularly [90,119]. In comparison to one-time purchase decisions that might have the side effect of increasing consumer's comfort, day-to-day actions implicate additional effort or decreased comfort.

Psychological factors including values, beliefs, attitudes and norms have been identified to be successful in predicting curtailment behavior [117,137]. For example, personal norms affect both curtailment behavior and involvement in purchase decisions through feeling a moral obligation to do so. This is also the case for environmental beliefs in the form of ascription of responsibility [118]. Eriksson et al. [138] and Nordlund and Garvill [139] have shown in their research on car use that there is a strong influence of personal norms for the willingness to curtail personal car use.

In general, Gatersleben et al. [137] and Whitmarsh [117] delivers an empirical evidence that daily energy saving actions are more likely to be influenced by internal factors, while actions which require considerable monetary costs (energy efficiency investments) are more dependent on guided circumstances. However, Jansson and Marell [118] shows in their empirical research that for both high involvement once-off actions and ongoing day-to-day actions biospheric values and personal norms have a strong influence on their energy reduction.

Regarding socio-demographic factors such as age, living status and gender, existing literature provides evidence both for and against hypothesis in either direction. Lee et al. [140] show that there are some gender differences in adoption of energy-efficient lighting at home in the sense that women are more likely to adopt energy-saving practices and were more willing to pay a higher price for energy-efficient light sources. Poortinga et al. [26] show that couples and families found technical efficiency measures more acceptable than singles did. According to Sardianou [112], energy saving investments are less likely to be made by older households since these households believe in shorter stream of benefits from energy improvements than other age cohorts. Another explanation is that younger households prefer an up-to-date technology which is most of the time also more efficient, while older households accept their older appliances and replace them only when necessary [114]. Carlsson-Kanyama et al. [114] also prove that households with younger head of the family are more likely to adopt energy-saving measures. However, Guerin et al. [120] show that age and the energy saving curtailment behavior is positively correlated. Poortinga et al. [26] also provides empirical evidence for the hypothesis that energy efficiency measures are more acceptable for households with a high income, while behavioral energy saving measures aimed at reducing direct energy costs were the least acceptable for high incomes. This might be explained, as seems to be straightforward, by the fact that energy efficiency measures (technical measures) often require an initial investment, which seems to be less problematic for households with a high income [112]. Another possible explanation for this phenomenon is the fact that day-to-day actions implicate a decrease in comfort while one-of actions might even increase consumer's comfort. Stern and Gardner [141], show that the home ownership also causes differences between households, energy efficiency investments is meaningful for homeowners whereas curtailments might be the only option for renters.

#### 3.3.4. Discussion

A number of key determinants have been identified in the literature, ranging from situational factors in the external environment (e.g., contextual, structural and institutional factors) through to more person-specific attributes of consumers themselves (e.g., socio-demographic, psychological factors). Despite an expanding literature, empirical evidence of the impact of the latter two broad categories of variables that have been identified, socio-demographic factors (e.g., income, employment status, dwelling type/size, home ownership, household size) and psychological factors (e.g., beliefs and attitudes, motives and intentions, perceived behavioral control, cost-benefit appraisals, personal and social norms) has not been consistent and conclusive to date. However, a common finding that has been well documented by behavioral economists, psychologists and other social scientists is that individuals do not always behave more sustainably despite having positive attitudes or behave logically to favorable economic choices in order to reduce household energy consumption.

Another common identified finding, combining financial incentives with program components (like energy assessments, information, education, appeals, informal social influences, convenience and quality assurance) reduce the transaction costs of targeted/ desired actions and have shown synergistic effects greater than the additive effects of individual interventions or policy. Furthermore, previous research has shown the importance of the full range of consistent knowledge of the environmental, economic and social impact for policy makers and financing institutions to decide whether or not to support new business models. For example, smart metering has been widely pushed, despite little knowledge on the environmental impacts as well as social impacts such as data security.

But both strands of action (one-off investment action or continuous action) require important and coordinated changes in household practices that go beyond passive assumption of energy-efficient technologies and acceptability of traditional policy measures. Efforts to change household energy use through information campaigns have proven very limited [24,129,142] and recent trends in diversification of energy generation and changing consumer roles have underlined the potential for smarter transformation potentials in harnessing the active households [143–145]. Nevertheless, little research is available on what individual and social factors might influence the adoption of novel energy consumption and investment practices in households and there is a stringent need to understand the barriers to and drivers of involvement in these. The challenge is to understand the internal, social and external level factors that threaten the energy use in household, so that energy-saving behaviors could be facilitated. Furthermore, the effects of contextual factors on energy usage behavior need to be studied in more detail, as well as how these factors might be affect various environmental and motivational factors. This in turn should lead to an extension of the existing methodological and/or theoretical models.

#### **4. A Framework to Understand Household's Energy Consumption**

Based on the literature review provided in the previous section, the identified key influencing factors are summarized as possibly affecting energy-saving behavior in a conceptual framework, presented on Figure 1. Whether household energy consumption is based on a one-off investment action or continuous actions, behavior is influenced by the external as well as by the internal context. External context such as institutional factors, technological developments, economic growth, cultural developments influence behavior at the broader level, while attitudinal and personal factors such as demographic factors and motivations shape behavior at the individual level. To illustrate the framework, the dimension of the external context, the internal context-attitudinal factors and the internal context-personal factors is connected. In order to differentiate between determinants of a different nature, a distinction between contextual, economic and social variables is proposed. These have different positions in the model and operate at different levels of influence. Regarding these levels of influence, personal (household) factors at the level of internal context have the biggest influence on the energy-saving behavior of households in contrast to the factors at the level of external context. A personal level economic factor can be altered relatively quickly, for example by a change in income, while the introduction of a subsidy program for all households in a country is often over the course of a few years.

**Figure 1.** Conceptual framework to understand households' energy efficiency behavior.

#### *Internal Context—Personal Factors*

As already explained, the characteristics of each household have a direct impact and the biggest influence on the behavior of households. Their characteristics reflect the attitude and experiences from a descriptive angle (e.g., ownership, size and type). The availability of the requirements necessary to adopt technical and behavioral measures (economic situation) is also an important issue. This obviously affects what a household can afford, but the perspective on money and the level of importance of price in certain purchasing decisions does not belong to personal factors but has to do with the attitude people have. The personality of the people is clustered in the social context, however, it is composed by role and status, age or gender which represents a strong connection for certain behavior.

#### *Internal Context—Attitudinal Factors*

Attitudinal factors include factors held by the individual that affect the choices and the behavior people undertakes. These include an individual's motivation (e.g., pro-social, altruistic), perception, beliefs and attitudes which are part of the contextual process affecting the individual intentions. It includes also calculations which people make before acting, including personal evaluations of costs and benefits. Thus in spite of the advantage of adopting more efficient appliances, the cost of that decision has to be in concordance with the perceived benefits. Even when energy-saving measures are affordable, the balance between costs and benefits could represent a major barrier due to the uncertainty. For instance, the fact of thinking about long-term benefits when costs are immediately perceived has a direct effect in the attitudinal factor regarding to the intention behavior (especially in the case of the adoption of photovoltaic panels, as shown in [99,146,147].

The lifestyle of people such as group membership, normative social influence and family are also important factors. The indirect commitment with society makes behavior to be on the same line with the others tended to follow social system flow. Also one has to cooperate with other household members.

#### *External Context*

The external environment comprises situational opportunities and dependence of other. It can be interpret as set of regulations, system of laws, political environment and governance structure which interrelated control the distribution and consumption of energy adopting new measures in households. At the social context for example, one has to obtain correct information about the most effective ways of reduction in order to reduce household energy consumption.

Table 4 provides an overview of the three categories of potentially important variables that have been identified for explaining variability in energy reduction of households: contextual, economic and social variables. These variables are divided over different levels with an explanation of the determinants of household energy behavior.



In summary, the conceptual model shows that energy consumption of households is based on a complex interaction between contextual, economic and social influence. This interaction has been structured into three categories implying a multilevel division of factors to shape the process of households' behavior and its transition to assume and adopt new insights affecting their day-to-day actions. The conceptual framework suggests a range of determinants for energy-saving behavior at different levels. However, it should be noted that an important point of attention is which specific label to be used in the conceptual framework and where the specific labels should be placed. This could be related to the disciplinary angle from which one approaches the framework. This is especially the case along the boundary of the social context. Although all the determinants are presented separately, from a practical approach are working synergistically and interrelated influencing the behavior and their current performance in households.

The framework is not only interesting for researchers, but also for policymakers (at the national and local level), practitioners (energy providers and engineers), as well as for social energy networks. First, it is interesting for policymakers in the area of energy provision for households, at national and local levels. At a national level, the gained insights into the "what", the "how" and the "why" provides handholds to formulate an appropriate policy or service view that can help the government to transform the current energy system into a more sustainable one. In order to motivate these

households, 'education and communication' is an important issue. Education on the interrelated issues of energy, climate change, and sustainability, and communication of strategies for reducing consumption and emissions (ranging from energy efficiency and conservation to more sustainable energy technologies). At a local level, households (and communities) can start participating more in bottom-up energy initiatives, thereby increasing the share of more sustainable energy technologies in the energy market. These results are also helpful for local governments and their planners as they have an important role to play in promoting more sustainable energy technologies. But, for both, national and local policymaker, these insights stresses the importance of creating policies that are transparent and easy to take advantage of. Second, we find that trustworthy information about the contextual (e.g., performance) and economic (e.g., costs) dimension is an important factor influencing interest in speaking with practitioners like energy providers and engineers. And finally, that households may seek such information from the experiences of personal connections in their neighborhoods and social networks (social dimension).

#### **5. Conclusions and Perspectives for Further Research**

Our intention in the paper has been to introduce a new conceptual framework to capture and understand households' energy consumption, efficiency behavior and curtailment behavior. Households have been progressively highlighted as the main actor that can play a substantial potential in the reduction of this energy use. Their behavior is a complex and hard to change process that combines numerous determinants. These determinants have already been extensively studied in the literature from a wide range of thematic areas each by its own assumptions and often neglect important energy behavioral components, therefore, energy behavior studies require an integration of disciplines through an interdisciplinary approach. Based on that knowledge, this paper aims at connecting objective (physical and technical) with subjective (human) aspects related to energy use of households in one framework. This combination should provide the answers to the 'what', the 'how' and most importantly the 'why' questions about people's behavior regarding energy use. This proposed framework allows clarifying the numerous internal and external factors that act as key determinants, as well as the need to take into account their interactions. Moreover, it would re-form demand as one of the result of interactions in and between the contextual, economic and social contexts in which households' lives. It would, however, not obviate the individual household nor research that intended to track changes in how individual households think and act. The framework proposed in this paper opens avenues for the integrated study of households' energy consumption and has further potential policy implications to better capture and take into account behaviors in policies, incentives and regulations still often focused on technical aspects.

Further studies are suggested to use the proposed framework for explaining households energy behavior focusing on identifying the specific factors that influence household energy usage (e.g., consumption) and changes in energy use over time (e.g., curtailment and efficiency behaviors). The framework has to be applied to an increasing set of empirical cases (for example PV and LED) carried out in a way as to systematically explore the opportunities and barriers, which in turn can enhance our understanding of how determinants interact as part of a larger explanatory framework.

**Author Contributions:** Conceptualization, V.V., A.-F.M. and V.U.; methodology, V.V. and A.-F.M.; validation, V.V., A.-F.M. and V.U.; formal analysis, V.V.; investigation, V.V.; resources, V.V.; data curation, V.V.; writing—original draft preparation, V.V.; writing—review and editing, A.-F.M. and V.U.; visualization, V.V.; funding acquisition, V.V.

**Funding:** This research is part of the research programme JSTP—Joint Research Projects: Smart Energy in Smart Cities, titled 'Energy efficiency of households in cities. A multimethod analysis' funded by the Netherlands Organisation for Scientific Research (NWO), grant number 467-14-023. Furthermore, the research presented in this paper also received funding from the European Union's H2020 Research and Innovation program under grant agreement number 727642. The sole responsibility for the content of this paper lies with the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Modeling Future Energy Demand and CO2 Emissions of Passenger Cars in Indonesia at the Provincial Level**

**Qodri Febrilian Erahman, Nadhilah Reyseliani, Widodo Wahyu Purwanto \* and Mahmud Sudibandriyo**

Chemical Engineering Department, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia **\*** Correspondence: widodo@che.ui.ac.id

Received: 9 July 2019; Accepted: 12 August 2019; Published: 17 August 2019

**Abstract:** The high energy demand and CO2 emissions in the road transport sector in Indonesia are mainly caused by the use of passenger cars. This situation is predicted to continue due to the increase in car ownership. Scenarios are arranged to examine the potential reductions in energy demand and CO2 emissions in comparison with the business as usual (BAU) condition between 2016 and 2050 by controlling car intensity (fuel economy) and activity (vehicle-km). The intensity is controlled through the introduction of new car technologies, while the activity is controlled through the enactment of fuel taxes. This study aims to analyze the energy demand and CO2 emissions of passenger cars in Indonesia not only for a period in the past (2010–2015) but also based on projections through to 2050, by employing a provincially disaggregated bottom-up model. The provincially disaggregated model shows more accurate estimations for passenger car energy demands. The results suggest that energy demand and CO2 emissions in 2050 will be 50 million liter gasoline equivalent (LGE) and 110 million tons of CO2, respectively. The five provinces with the highest CO2 emissions in 2050 are projected to be West Java, Banten, East Java, Central Java, and South Sulawesi. The projected analysis for 2050 shows that new car technology and fuel tax scenarios can reduce energy demand from the BAU condition by 7.72% and 3.18% and CO2 emissions by 15.96% and 3.18%, respectively.

**Keywords:** energy demand; CO2 emissions; Indonesia

#### **1. Introduction**

Since 2013, the transport sector has consumed more energy than any other sector in Indonesia. Approximately 40% of the energy demand (260.1 million BOE) in Indonesia is attributable to the transport sector [1], with road transport being the largest contributor. This situation is predicted to increase, due to the growth of car ownership.

Transportation plays an important role in modern society in terms of supporting the mobility of people; however, it also creates a major problem for the environment. CO2 emissions in the road transport sector are mostly contributed by the use of passenger cars. This situation is worsened by the lack of improvements to the land transportation system. To ensure mobility under the present circumstances, most people choose to own a private car. The growth in car ownership is considered to be mainly responsible for rising energy demand. Passenger cars in Indonesia mostly consume gasoline, and high demand for gasoline has resulted in Indonesia's dependence on imported petroleum products [2]. Car ownership has a strong correlation with GDP per capita, as shown in many previous studies, including Dargay and Gately [3], Dargay and Gately [4], Dargay and Gately [5], Dargay, Gately and Sommer [6], Leaver, Samuelson and Leaver [7], and Wu, Zhao and Ou [8]. These studies suggest that the GDP per capita can affect the level of energy demand.

The issuing of Presidential Decrees 61/2011 and 71/2011 [9,10] mandated a mitigation plan for greenhouse gas emissions for each province. Based on these regulations, provincial governments were asked to prepare action plans to the reduction of CO2 emissions. The action plans can be carried out by controlling the intensity and activity of passenger cars. The intensity is related to car technology, while car activity is related to car utilization. Certain policies for controlling the intensity and activity of passenger cars should be encouraged in order to decrease energy demand and CO2 emissions [11,12]. Therefore, the historical energy demand from the use of passenger cars in each province should be known.

Previous studies have shown that transport energy demand can be projected through top-down models (e.g., Zhang et al. [13], Lu et al. [14], and Chai et al. [15]); however, to determine the impact of technological change, the energy demand projection for the road transport sector should be conducted using a bottom-up model [16]. Other studies have implemented a bottom-up model for projecting the transport energy demand (e.g., Eom and Schipper [17], Ma et al. [18], Baptista et al. [19], Ko et al. [20], and Deendarlianto et al. [21]). However, these studies have mostly been conducted at country level, whereas, because disparities exist among regions, this study was conducted at the provincial level. Moreover, the study contributes to estimating the passenger car energy demand by modeling the technological changes and the activities of the passenger car and to find out which is the best policy for lowering the energy demand and CO2 emissions. This paper aims to model the future energy demand and CO2 emissions of passenger cars in Indonesia by province in past (2010–2015) and future (2016–2050) periods.

The remainder of this paper is structured as follows. Section 2 proposes the methodology. Section 3 presents the results and discussion, and Section 4 provides the conclusions.

#### **2. Methods**

This section explains the methodology for assessing future energy demand and CO2 emissions using a bottom-up model. Figure 1 explains the methodological structure of the current study.

**Figure 1.** Schematic diagram of the methodology used.

As can be seen in Figure 1, the structures consist of the input, the model and the output. Input includes everything that is to be processed in the model, including data and scenarios. The model consists of car ownership, vehicle kilometers traveled (VKT), and weighted average fuel economy. These aspects of the model will generate the intermediate output of VKM and fuel economy, from which the fuel demand and CO2 emissions are derived. This structure is applied for each province, and subsequently, the results are aggregated to obtain the national results.

#### *2.1. Provinces of Indonesia*

Administratively, Indonesia consists of 34 provinces, but the current study analyzed only 33 to adjust to the available data, and also because of the emergence of new provinces in Kalimantan. Each province has its own local government, governor, and legislative body. Spatially, Indonesia can be divided into five major regions: Sumatra, Java, Kalimantan, Sulawesi, and Nusa Tenggara-Maluku-Papua. Table 1 presents the related details.



Figure 2 shows the profile of Indonesia's territories according to their populations, which are highly concentrated in the west. The capital city of Indonesia known as the Special Capital Region of Jakarta (DKI Jakarta) is located in the Java region, contributing to the fact that this region is the most densely populated. These population trends are expected to continue if the government does not promote greater equity among the provinces.

**Figure 2.** Spatial population profiles among regions in Indonesia.

#### *2.2. Input Data*

Data such as provincial GDP, the number of passenger cars, the size of province area, and population are sourced from the Central Bureau of Statistics of Indonesia [22]. Energy demand for the transport sector, along with fuel price data, were collected from the Ministry of Energy and Mineral Resources of Indonesia [1]. Annual car sales data, which are categorized by engine displacement, were obtained from the Association of Indonesian Automotive Industries (Gaikindo) [23]. The Central Bureau of Statistics of Indonesia provides population projections until 2050 [24], and the projected provincial population takes into account the effect of urbanization. The provincial GDPs are based on commodity prices in the year 2000, and the projections are obtained using GDP growth until 2050 [25]. Finally, the data is inputted into the model.

#### *2.3. Car Ownership*

Car ownership exhibits a close relationship with GDP per capita [4]. This empirical relationship follows the Gompertz model, which has been developed in various studies [3–6]. It explains that, over the long term, the relationship between car ownership and GDP per capita corresponds to the following equation:

$$\text{CO}\_{i} = \text{CO}\_{i}^{\*} \times \varepsilon^{a\_{i}\bar{\sigma}\_{i}^{\text{G-LMPP}\_{i}}} \tag{1}$$

where *CO* is the car ownership (vehicles/1000 people), *CO\** is the saturated car ownership, *GDPP* is GDP per capita, *i* is the province, and α and β are the constants that determine the shape of the curve. The constants α and β can be obtained according to the following equation [8].

$$\ln\left(\ln\frac{CO\_i^\*}{CO\_i}\right) = \ln(-\alpha\_i) + \beta\_i \cdot GDPP\_i \tag{2}$$

In the equation, α and β are constants to determine the curve shape. The relationship between GDP per capita and long-term car ownership forms an S-shaped curve. This S-shape implies that at a relatively low level of GDP per capita, the growth rate of car ownership will rise slowly, then will grow dramatically at a certain GDP per capita level, and will finally slow down again at a high level of GDP per capita until reaching a steady state, which is known as car ownership saturation [5].

The car ownership saturation is a condition in which GDP per capita continues to increase, while car ownership remains unchanged. Previous studies have suggested that there is a relationship between population density and the saturation level of car ownership [7]. For example, Leaver established a relationship between population density and car ownership saturation [7]. The higher the population density, the faster car ownership saturation occurs, and the current study uses this finding to determine the saturation level of car ownership for each province, as shown in the following equation:

$$\text{CO}^\* = 606.5e^{(0.007 \times D)} \tag{3}$$

where *D* is population density. Since the analysis is conducted at the provincial level, the effects of urbanization have been included in the projected population data. Figure 3 summarizes the scheme of the car ownership projection model.

**Figure 3.** Scheme of the car ownership projection model.

#### *2.4. Car Fuel Economy*

Fuel economy is reported in units of L/100 km. National fuel economy is calculated from the weighted average of new and existing car shares and their respective fuel economies. The fuel economy of new cars is taken from a weighted average of annual car sales by fuel type, i.e., gasoline vs. diesel

cars. Fuel economy is further characterized according to engine size: 800 < cc < 1200, 1200 < cc < 1500, 1500 < cc < 3000, and 3000 < cc for gasoline cars; and 1500 < cc < 3000 for diesel cars. Cars with an engine size of 800 < cc < 1200 are referred to as low-cost green cars (LCGC) [26].

In the projected scenario, due to the presence of new car technology (e.g., plug-in hybrid [PHEV] and electric vehicle [EV] technology), the fuel economy of a new car is weighted by the share of each type of car—gasoline, diesel, PHEV, and EV—according to the following equations.

$$FE\_{\rm NC} = \sum\_{j} FE\_{j} \times \%C\_{j}.\tag{4}$$

$$FE = FE\_{\rm NC} \times \%C\_{\rm NC} + FE\_{\rm RC} \times \%C\_{\rm RC} \tag{5}$$

where *FE* is fuel economy, *%C* is the percentage of cars, and *j* is the type of car based on its technology (e.g., gasoline, diesel, PHEV, or EV). *NC* is new car and *RC* is the rest of the cars. Figure 4 describes the fuel economy aggregation scheme based on car technology.

**Figure 4.** Fuel economy aggregation scheme based on car technology.

The historical fuel economy (2010–2015) for an engine size of 800 < cc < 1200, which is in the LCGC category, is 5.0 L/100 km [27]. Cars with engine sizes of 1200 < cc < 1500, 1500 < cc < 3000, and 3000 < cc have the highest market share and fuel economies of 8.20 L/100 km, 10.10 L/100 km, and 12.40 L/100 km, respectively [28–30]. Diesel cars, which have a fuel economy of 6.97 L/100 km [12], are considered to be 20% more efficient than gasoline cars. Car fuel economy for engine sizes 1200 < cc < 1500 and 1500 < cc < 3000 was contributed by sedan and MPV (Multi-Purpose Vehicle) types of vehicle, while for cars with engine size 3000 < cc, this was contributed by Sedan and SUV (Sport Utility Vehicle) types. The percentages of sedans, MPVs and SUVs are 6.1%, 93.2, and 0.6% of total cars, respectively.

The fuel economy for PHEV and EV cars was not applied in the historical situation, since their market share was zero until 2015. Figure 5 describes the aggregation scheme of the weighted average of fuel economy between new and other cars.

Fuel economy for new cars is considered starting in 2010; for the remainder of the cars, fuel economy before 2010 is assumed based on the IEA report [31].

**Figure 5.** Aggregation of fuel economy between new and other cars.

#### *2.5. Vehicle Kilometers Traveled*

Vehicle kilometers traveled, VKT, is defined as the annual kilometers traveled for a single car. Previous studies show an inverse relationship between VKT and fuel price, meaning that car users will tend to reduce unnecessary travel when the fuel price increases. The extent to which VKT varies with changing fuel price can be modeled by the value of elasticities, according to the following equation [32].

$$VKT\_i = VKT\_i' \times \left(\frac{FC\_i}{FC\_i'}\right)^{\varepsilon} \tag{6}$$

where *VKT*. represents the vehicle kilometers traveled in a given year, *VKT* . is the vehicle kilometers traveled in the previous year, *FC*. is the fuel cost in a given year, *FC* is the fuel cost in the previous year, and ε is the elasticity. VKT data per province can be obtained through calculations of fuel consumption, fuel economy, and number of vehicles in the historical year (2012–2015). Previous studies described that annual car travel is also influenced by car fuel economy [33]; therefore, the current study prefers to use fuel cost instead of fuel price in order to more effectively assess the impact of real situations on the behavior of private car users. Fuel cost is described as the retail fuel price multiplied by the national fuel economy. In the projection, the retail fuel price is obtained by the summation of crude oil price, refinery margin, and distribution fees to customers, and fuel taxes. Crude oil price is based on the US Energy Information Administration outlook [34], and the refinery margin follows the Asia refining margin outlook [35]. Meanwhile, the distribution cost is assumed to remain constant [36]. The sum of total cars traveling in a certain year is defined as car activity, *VKM*.

#### *2.6. Energy Demand and CO2 Emissions*

Energy demand is defined in units of liter gasoline equivalent (LGE). Cars that consume other fuels, such as diesel oil, should be converted into LGE using heating value comparisons between gasoline and diesel oil, where the heat value for diesel, biodiesel and gasoline is 35,327, 36,131 and 31,795 kJ/L, respectively. Energy demand can then be calculated according to the following equation:

$$E\_i = V\text{KM}\_i \times FE.\tag{7}$$

where *E* is the energy demand, and *VKM* represents vehicle kilometers, which represents the total number of cars traveling annually. Once the energy demand is determined, then CO2 emissions can be calculated using the following equation:

$$G\_i = E\_i \times EF\_k \tag{8}$$

where *G* represents the CO2 emissions, *EF* is the emission factor, and *k* is the type of fuel (e.g., gasoline, diesel oil, and electricity). Equations (7) and (8) are consistent with the ASIF equation, which is widely used for calculating CO2 emissions. Emission factors were obtained from the Ministry of Environment of Indonesia, which in turn based them on information from the Intergovernmental Panel on Climate Change (IPCC). Therefore, the emission factors for gasoline, diesel, biodiesel B100, and electricity were 69.3, 74.1, 62.9, and 224.4 kg CO2/GJ, respectively [37,38]. Moreover, the electricity emission factor was based on the weighted-average data from all kinds of power plants in Indonesia [38].

#### *2.7. Model Validation*

The results of the analysis need to be validated to determine the accuracy of the model. This is accomplished by comparing the results with the fuel demand in 2010–2015 using the standard error of the estimate. The standard error of the estimate is a measure of the accuracy of predictions. It indicates how far data points are from the prediction line of the average. The following is the equation of the standard error of the estimate.

$$
\sigma\_{\text{est}} = \sqrt{\frac{\sum \left( E - E' \right)^2}{N}}
$$

where σ*est* is the standard error of the estimate, *E* denotes the data points, *E'* is the predicted value, and *N* is the number of data points.

#### *2.8. Scenarios*

Scenarios for reducing CO2 emissions from car utilization can be developed by managing the intensity and activity of cars. Controlling the intensity of cars can be achieved by encouraging the uptake of new technologies that allow for better fuel economy and emissions reduction. Therefore, the market share of new cars with better fuel technology should be increased in order to improve fuel economy. To purchase the most efficient cars in the market, consumers must first understand the efficiency features of the cars under consideration [39]. Therefore, fuel economy labeling should become a required policy to support the introduction of new car technologies that enable better fuel economy. Fuel economy labeling is carried out by obligating car manufacturers or dealers to provide information on the fuel economy of new cars. Car labeling policies are also useful as an important basis for other policies, such as fuel economy standards [12].

Car activity can be managed by regulating the fuel price, so that car users will limit unnecessary travel. The policy required to support this scenario is fuel taxes arrangement [12]. Fuel taxes are an appropriate policy for reducing car travel, because the higher the fuel prices are, the more people will reduce car travel, especially for unnecessary trips. Fuel taxes can provide significant incremental incentives to save fuel and can be integral to any policy package to promote sustainable transport, whereas fuel subsidies are considered to be counterproductive [12]. Fuel taxes also provide revenues to pay for infrastructure costs and to develop sustainable transport. Therefore, scenarios exploring these various policies are created in the current study and are divided into three parts: BAU, new car technology, and fuel tax regulation. These scenarios are intended for use in the projections from 2016 to 2050.

#### a. Business as Usual Scenario (BAU)

This scenario assumes that the available car technology is limited to gasoline and diesel cars; however, new car fuel economy is expected to improve. Projections for technological developments

related to new car fuel economy follow recent developments in non-OECD countries for fuel economy improvement rates [31]. Fuel economy improvement can be applied for gasoline and diesel cars until 2050. The share of cars based on technology follows the historical pattern (2015), in which the shares of car sales for gasoline and diesel cars are 83% and 17%, respectively. For PHEV and EV, on the other hand, the sales remain at zero due to the lack of government initiatives encouraging sales. In the BAU scenario, the fuel tax percentage follows the current situation, which is 15% of the fuel price, and it is assumed that there will be no change in the following years.

b. Car Technology Scenario

The car technology scenario is related to the government's national energy plan for the market penetration of electric vehicles, as stated in Presidential Decree 22/2017 [40]. This scenario assumes that market penetration for PHEV and EV cars is growing significantly. The penetration for PHEV and EV cars follows the IEA's Blue Map scenario [41], wherein to reduce significant global emissions, it is necessary that the 2050 sales mix for PHEV and EV is equal to at least half of total annual car sales [41]. Therefore, the sales mix for PHEV and EV in 2050 is targeted at 50%, while the remaining 50% constitutes mixed sales of diesel and gasoline cars. Table 2 describes the percentage of car sales by type and scenario. The success of car technology scenarios for CO2 emission reduction hinges on the significant decrease in the electricity emission factor. Based on the Blue Map scenario, the electricity emissions factor should be decreased to almost zero in 2050 [41]; therefore, the electricity emission factor for the car technology scenario is assumed to decrease gradually, reaching 27.8 kg CO2/GJ in 2050. The target of reducing the emission factor of the electricity can be conducted by increasing the supply of electricity from renewable sources, i.e., geothermal, hydro, solar, wind and biomass.

**Table 2.** Comparison of percentages of new car sales by car technology and scenario.


#### c. Fuel Tax Regulation Scenario

This scenario aims to study the effect of car activity on energy demand through the regulation of fuel tax. Changes in fuel cost could affect the VKT, which in turn could affect the VKM. The responses of car users to rising fuel costs are different in each province, and this is indicated through the elasticity. In 2015, the decrease in global crude oil prices caused a decline in fuel prices. The government took advantage of this situation by eliminating fuel subsidies, particularly for the transport sector. Since then, the government has imposed an economic price for gasoline. After the cessation of subsidies, tax policy became recognized as an effective instrument for controlling car travel. Currently, the two kinds of applied fuel tax are value added tax and motor vehicle fuel tax, with values of 10% and 5% of the retail price, respectively. Therefore, the total applied accumulated tax is 15% of the retail price.

A comparison with other countries in the ASEAN region shows that in 2012, the total tax related to fuel demand in these countries ranged from 4–36% [42]. Therefore, to make our scenario more plausible, the fuel tax was set at 30%. The fuel tax scenario assumes no changes in the share of new car sales, and the fuel economy of new cars follows the BAU scenario. Therefore, any changes in energy demand and CO2 emissions are due solely to changes in car activity. Table 3 summarizes the comparison of assumptions among scenarios.


**Table 3.** Comparison of assumptions among scenarios.

#### **3. Results and Discussion**

#### *3.1. Historical Results*

#### 3.1.1. GDP Per Capita

GDP data were collected from 2000 to 2015. The national GDP is an aggregation of all provincial GDPs. Each province contributes independently to the national GDP, and there are disparities among provinces. Based on provincial GDP data, it can be determined that 57% of the national GDP is from DKI Jakarta, East Java, West Java, and Central Java. However, the prosperity level is more suitably represented by GDP per capita. Table 4 describes the GDP per capita for each province.


**Table 4.** GDP per capita of provinces, 2000–2015 (Rp).

#### 3.1.2. Car Ownership

Table 5 shows car ownership levels in each province between 2000 and 2015. It shows that the province with the highest car ownership level is DKI Jakarta. Other provinces with substantial car ownership levels are Bali, Central Kalimantan, and Riau.


**Table 5.** Car ownership in provinces, 2000–2015 (Vehicles/1000 People).

According to the Gompertz model, in long-term projections, car ownership will form an S-curve. The differences in the S-curve shape in each province will depend on the value of α, β, and the saturation level for car ownership. The values of α and β are strongly influenced by the historical relationship between car ownership and provincial GDP per capita, while the saturation level for car ownership will be different in each province due to differences in population density.

Table 6 shows the results of the car ownership analysis, which pertain to the car ownership model and are based on the historical situation, particularly from 2000 to 2015. The R<sup>2</sup> value shows the accuracy of α and β in the linearized Gompertz model (Equation (2)).


**Table 6.** Results of car ownership analysis by province using the Gompertz model.

The α value indicates that the Gompertz curve shifts either to the left or to the right along the *x*-axis. The lower the value of α, the more the Gompertz curve shifts to the right along the *x*-axis, and thus, the more distant it gets from a saturated condition. The β value indicates the growth rate of car ownership for certain year ranges. The smaller the β is, the higher is the car ownership growth.

Car ownership saturation shows an asymptotic value, where car ownership is in the steady state. As depicted in Table 6, DKI Jakarta has the lowest car ownership saturation level, due to having the highest population density. Therefore, DKI Jakarta will be the first province that will experience saturation.

#### 3.1.3. National Car Fuel Economy

Figure 6 shows the market shares of gasoline cars sold by engine size during 2010–2015. It shows a decline in the share of cars with engine sizes of cc < 1500 and 1500 < cc < 3000 and an increase in the share of cars with an engine size of 800 < cc < 1200 (LCGC). During 2013–2015, the increase in LCGC accounted for a decrease in the sales of cars with larger engine sizes. Figure 6 also shows the shares for gasoline vs. diesel cars during 2010–2015. The higher level of current diesel car sales is because several car manufacturers have started to offer diesel technology in their vehicles. In contrast, PHEV and EV are still not commercially available in the Indonesian automobile market, and therefore their shares remain at zero.

**Figure 6.** Shares of car sales by (**a**) engine size (gasoline cars) and (**b**) engine type.

Based on market share data, the national car fuel economy showed a decline, as shown in Figure 7. The accumulated car fuel economy describes the average fuel economy for all cars in Indonesia, while the car sales fuel economy describes the fuel economy only for cars that were sold in a given year. Fuel economy for sold cars improved after 2012, which was mainly due to the increasing number of LCGC cars. The fuel economy discrepancy between sold cars and accumulated cars is in the range of 1–1.56 L/100 km, where this discrepancy is estimated to be larger throughout the years.

**Figure 7.** Fuel economy of sold cars and accumulated cars.

#### 3.1.4. Vehicle Kilometers Traveled

Vehicle kilometers traveled, VKT, exhibits disparities between provinces, as seen in Table 7, which indicates the changes in the historical VKT during 2012–2015. VKT changes as fuel cost changes, and the magnitude of thoses changes depends on elasticity.

VKT declines in provinces due to increases in fuel cost. The fuel economy improvement, as shown in Figure 7, is unable to offset the increase in fuel price. Therefore, the total fuel cost is still increasing. Elasticities in the provinces range from −0.067 to −1.051. Elasticity greater than 1 indicates an elastic change in VKT when there is a slight change in fuel cost. An elasticity value less than 1, on the other hand, indicates a small change in VKT with a change in the fuel cost. The East Kalimantan province shows perfect elasticity; therefore, the changes in the fuel cost will be proportional to the VKT changes. Moreover, the highest VKT is observed in Banten. This may be due to Banten's adjacency to the central capital region of DKI Jakarta. Consequently, Banten has many residents who are commuters; these people live in Banten but work in DKI Jakarta.


**Table 7.** Vehicle Kilometers Traveled in Provinces, 2012–2015 (km/car/year).

#### 3.1.5. Energy Demand and CO2 Emissions

The energy demand for provinces tends to increase from 2010 to 2015, as depicted in Table 8. The five provinces with the highest energy demand, i.e., West Java, East Java, DKI Jakarta, Central Java, and Riau, are quite similar to the top five provinces in GDP rating. This shows that more than 50% of car energy demand arises from the Java region.

National energy demand is an aggregation of energy demand for all provinces. As depicted in Figure 8, national energy demand increased by 29% from 2010–2015, while GDP increased by 34% for the same period. In other words, energy demand and GDP increased almost proportionally during this time. Although energy demand showed a gradual steady increase, stagnation occurred during 2013–2015. This was caused by the increase in gasoline prices due to government regulation, with the result being that most people reduced unnecessary travel.

The CO2 emissions profile is quite similar to that of energy demand and shows a gradual increase from 2010 to 2015. About 95% of the total emissions were from gasoline cars, and the remainder were from diesel cars. The emissions from diesel cars resulted from the consumption of a fuel mix of diesel oil and biodiesel that was mandated by the Ministry of Energy and Mineral Resources Regulations 32/2008 and 25/2013 [43,44]. Biodiesel mix usage increased from 1% in 2010 to 10% in 2015. The mandatory biodiesel mix regulation played a role in CO2 emissions reductions in 2010 and 2015, which were 0.02% and 0.11%, respectively.



**Figure 8.** Historical (**a**) energy demand and (**b**) CO2 emissions, 2010–2015.

However, efforts for reducing CO2 emissions can be more easily understood through examination of the intensity of CO2 emissions per car activity. In 2010, the CO2 emissions intensity per car activity was 207 g CO2/km, while in 2015 it decreased to 198 g CO2/km. This indicates a gradual decline of 0.94% per year.

With respect to emissions intensity per car activity, a comparison between countries listed on the International Council on Clean Transportation (ICCT) report in 2010 showed the following: in Asian countries such as Japan, India, China, and South Korea, it was in the range of 130–180 g CO2/km; for countries in the Americas, such as the United States, Canada, and Mexico, it was in the range of 180–220 g CO2/km; and for the European Union, it was 135 g CO2/km [45]. Based on these comparisons, the CO2 emissions intensity per car activity in Indonesia can be said to be high. Therefore, more efforts should be undertaken to significantly reduce CO2.

#### 3.1.6. Model Validation

Validation compares other data with the results for the provincial and national models. Looking at the standard error of results for 2010–2015, the provincial model has a standard error of estimates 0.0326, while the national model's was 0.0516. This finding demonstrates that the accuracy of the provincial model is higher than the national model. Figure 9 illustrates the comparison of energy consumption between the model results and the data from Ministry of Energy and Mineral Resources of Indonesia [1].

**Figure 9.** Comparison of energy consumption between data with model results. (**a**) Energy consumption; (**b**) Percentage.

#### *3.2. Projection Results*

#### 3.2.1. Projection of Car Ownership

Figure 10 shows car ownership projections for provinces grouped by region. These projections show disparities among provinces. In 2015, the difference of car ownership among provinces was in the range of 3–344 vehicles/1000 people, with the average car ownership across provinces being 64 vehicles/1000 people. In 2050, the discrepancy is expected to widen, with an estimated range of 117–603 vehicles/1000 people and average car ownership across provinces at 479 vehicles/1000 people. In 2050, the smallest discrepancy is expected to appear for the Kalimantan and Sumatra regions, and the largest for the Nusa Tenggara, Maluku, and Papua regions. The provinces of Maluku and North Maluku, which are mostly situated on an archipelago, show relatively low rates of car ownership. The first province to experience car ownership saturation is DKI Jakarta, with most provinces approaching the saturated condition and a few more that are just starting to approach saturation.

Figure 11 shows a comparison of the top five provinces by number of cars. In 2015, the number of cars in Jakarta was the highest, but in 2050, Jakarta is not expected to be in the top five, because car ownership in Jakarta has already reached saturation, with the population at its maximum level. In 2050 it is also expected that approximately 50% of cars will continue to be concentrated in the Java region.

**Figure 10.** Projection of car ownership in (**a**) Sumatra (**b**) Java (**c**) Kalimantan (**d**) Sulawesi (**e**) Nusa Tenggara, Maluku, Papua.

**Figure 11.** Comparison of the Top 5 provinces by number of cars (**a**) in 2015 (**b**) in 2050.

#### 3.2.2. Impact of Policy Scenario

The BAU scenario is used as a reference for the other scenarios in terms of energy demand and CO2 emissions reduction. The differences between the BAU scenario and other scenarios are in the intensity and activity of cars; therefore, fuel economy and VKT will also differ among scenarios. Fuel economy in the BAU scenario shows an improvement, as depicted in Figure 12.

**Figure 12.** Projected National Fuel Economy, 2016–2050.

Fuel economy improvement in the projected BAU scenario occurs because car manufacturers are expected to improve their fuel economy regardless of the enactment of specific policies. However, this improvement in fuel economy is not as significant as in the car technology scenario. The car technology scenario leads to significant improvement in fuel economy. According to a previous study [46], fuel economy improvements can occur even if technological developments for increasing vehicle efficiency are only directed at improving fuel economy, and the performance of the vehicle remains constant. This study has analyzed possibilities in fuel economy improvement through modifications such as decreasing the weight and size of the car, in the absence of technological developments that increase the acceleration and horsepower performance [46]. These kinds of modifications are used in the assumptions of car fuel economy improvements for the car technology scenario.

The VKTs decrease slightly in the BAU scenario due to fuel price increases. Changes in fuel prices are more likely to occur as crude oil price increases, according to the crude oil price projections reported by the US Energy Information Administration [34]. Table 9 shows the *VKM* at BAU conditions for each province.


**Table 9.** *VKM* projection results for provinces, BAU scenario, 2016–2050 (million *VKM*).

The *VKM* projections in the BAU scenario show disparities among the provinces. In 2050, the five provinces with the highest *VKM* will be West Java, East Java, DKI Jakarta, Central Java, and Riau. National *VKM* is an aggregation of the *VKM* of all provinces. The comparison of national *VKM* among the different scenarios is shown in Figure 13.

**Figure 13.** Comparison of VKM among the scenarios: (**a**) 2016–2050, and (**b**) 2050.

Based on Figure 13a, the fuel tax scenario has the lowest value for VKM. The fuel tax scenario reduces VKM by 3.18%, while the VKM in the car technology scenario tends to be higher than in the BAU scenario, because the significant fuel economy improvement causes the fuel cost to decrease. Consequently, this may precipitate an increase in VKM. This effect is commonly referred to as a rebound effect, such that fuel economy improvement does not reduce energy demand but instead increases it.

The energy demand projections for all provinces are shown in Table 10. The top five provinces in terms of energy demand increase are North Maluku, Southeast Sulawesi, Banten, Papua, and Lampung. These increases are caused by the growth rate of car ownership, which is influenced by a combination of α and β and also by the high VKT in preceding years. The highest energy demand is predicted to occur in 2030, because a take-off phase in levels of car ownership is expected in many provinces in that year.


**Table 10.** Energy demand projections for provinces, BAU scenario, 2016–2050 (LGE).

In DKI Jakarta, the energy demand tends to be stable, even decreasing in 2050. This decrease is due to the fuel economy of cars, which continues to decline from year to year, while car ownership remains stable because of the steady population. According to the projections from the Central Bureau of Statistics of Indonesia, in 2050 DKI Jakarta's population is predicted to increase by only 14%, while the average population growth throughout all provinces will be approach 41%. This means the number of cars in DKI Jakarta cannot increase significantly. As a result, decreases in fuel economy would be able to offset the increase in VKM, while for the other provinces, the reverse situation applies. Figure 14 shows the comparison between scenarios for energy demand.

*Energies* **2019**, *12*, 3168

**Figure 14.** Results of energy demand and CO2 emissions among scenarios. (**a**) Energy Demand (BAU Scenario); (**b**) CO2 Emissions (BAU Scenario); (**c**) Energy Demand (Car Tech. Scenario); (**d**) CO2 Emissions (Car Tech. Scenario); (**e**) Energy Demand (Fuel Tax Scenario); (**f**) CO2 Emissions (Fuel Tax Scenario).

The BAU scenario projections show that in 2050, the energy demand and CO2 emissions will reach 50 million LGE and 110 million tons, respectively. This situation is about 4.3 times higher than in 2015. Moreover, the energy demand in the car technology and fuel tax scenarios will reach 46 and 49 million LGE, while the CO2 emissions will reach 93 and 107 million tons, respectively. Figure 15 shows the comparison of CO2 emission reduction in 2050 among all scenarios. The highest performance in terms of CO2 emissions reduction occurs in the car technology scenario. The car technology scenario shows greater reduction due to the sales mix of PHEV and EV reaching 50% in 2050, with the accumulated number of PHEV and EV cars reaching 17.6 million, or 18% of the total car population. Moreover, the large number of CO2 emission reductions in the car technology scenario occurred due to significant decarbonization of the electricity generation and share technology vehichle.

**Figure 15.** Comparison between scenarios for energy demands and CO2 emission savings (**a**) Energy demand savings; (**b**) CO2 emission savings.

To realize this market penetration of PH/EV, several problems need to be overcome: limited battery car capacity, the cost of batteries, charging infrastructure, economies of scale, and the total cost of operating the PH/EV against liquid fuel car operation. The government needs to devise better strategies, including a roadmap outlining battery charging infrastructure, fiscal policies to reduce the total cost of PH/EV, in order to create a more competitive market for the PH/EV cars. Further strategy to be implemented is green incentives to increase the willingness to pay of the electric vehicle, therefore the electricity vehicle's ownership will be increased.

The fuel tax scenario reduces CO2 emissions through VKM reduction. Since 2015, the government has eliminated subsidies, demonstrating that a fuel tax can be an effective means to control car travel. A tax of 30% could reduce CO2 emissions by 3.18%. However, the tax regulation should take into account the people's purchasing power. Therefore, the government should increase the people's purchasing power and consider fuel price based on fuel quality. Figure 16 shows the expected CO2 emissions disparities among provinces in 2050.

**Figure 16.** CO2 emissions disparities among provinces, BAU scenario, 2050 (million ton CO2).

The disparity of CO2 emissions among provinces is quite striking, especially the disparity between western and eastern Indonesia. Special attention should be given to western Indonesia, then, particularly the Java region. The five provinces expected to contribute the most to CO2 emissions by 2050 are West Java, East Java, Central Java, Banten, and South Sulawesi. The CO2 emissions in DKI Jakarta are not expected to change much, while adjacent provinces are likely to experience high CO2 emissions.

In 2050, the values for CO2 emissions intensity per car activity for the BAU and car technology scenarios are 145 and 114 g CO2/km, respectively, while the values for the fuel tax scenario are similar to those for the BAU scenario. The car technology scenario shows a significant improvement, with 15.96% lower emissions than in the BAU scenario. However, such emission reductions require a significant reduction in electricity emission factors to be near zero kg CO2/GJ by 2050 which can be done through increasing the supply of electricity from renewable energy sources.

#### **4. Conclusions**

This study analyzes energy demand and CO2 emissions in Indonesia in a historical situation (2010–2015) and during a projected period (2016–2050) resulting from the use of passenger cars. The results show disparities among provinces, which are mainly due to differences in GDP, population, area, and the number of cars. The historical situation shows that in 2015, the energy demand and CO2 emissions from passenger cars amounted to 10 million LGE and 23 million tons of CO2, respectively. In 2050, these values are expected to reach 50 million LGE and 110 million ton of CO2, respectively, which is 4.3 times higher than that in 2015.

The five provinces with the highest CO2 emissions in the historical situation, particularly in 2015, are West Java, East Java, DKI Jakarta, Central Java, and Banten. In 2050, the top five are predicted to be West Java, Banten, East Java, Central Java, and South Sulawesi. Therefore, special attention needs to be accorded to these provinces.

Compared to the BAU condition, the car technology and fuel tax scenarios could reduce energy demand by 7.72% and 3.18% and CO2 emissions by 15.96% and 3.18%, respectively. The car technology scenario requires certain policies in order to achieve the reduction in CO2 emissions, such as car economy labeling and fuel economy standards. Economy labeling is an obligation for car manufacturers and dealers to provide information on car fuel economy, while fuel economy standards are enacted by limiting car fuel economy based on the vehicle's class and intended purposes. In addition, this scenario requires a significant reduction in electricity emission factors to be 27.8 kg CO2/GJ by 2050.

The projected fuel tax scenario could reduce CO2 emissions by 3.18% in 2050. This scenario could be realized by imposing higher taxes in order to limit car activity. The higher the tax, the lower the CO2 emissions; however, the imposition of fuel tax should also consider the ability of people to buy fuel, which is in line with GDP per capita.

The model for energy demand and CO2 emissions of passenger cars at the provincial level can improve the accuracy of the analysis when aggregated to the country level, which is proven by model validation.

The current study's results could be used by provincial governments as an overview of energy and CO2 emissions contributions by passenger cars. Furthermore, some scenarios have been given to illustrate possibilities for CO2 emissions reduction. Special attention should be given to provinces which are the largest contributors to the current problem and also to those expected to experience significant increases in CO2 emissions in the future.

**Author Contributions:** Conceptualization, W.W.P.; Methodology, Q.F.E.; Validation, W.W.P., and M.S.; Formal Analysis, Q.F.E.; Investigation, Q.F.E.; Data Curation, Q.F.E.; Writing-Original Draft Preparation, Q.F.E.; Writing-Review & Editing, M.S., N.R.; Visualization, Q.F.E.; Supervision, W.W.P.; Project Administration, W.W.P.; Funding Acquisition, W.W.P.

**Funding:** This research was funded by the Directorate for Research and Public Services (DRPM) Universitas Indonesia, grant number Hibah Publikasi Artikel di Jurnal Internasional Kuartil Q1 dan Q2 (Q1Q2) NKB-0328/UN2.R3.1/HKP.05.00/2019.

**Conflicts of Interest:** The authors declare no conflict of interest.

*Energies* **2019**, *12*, 3168

#### **Nomenclature**


#### **References**


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Tel: +41 61 683 77 34 Fax: +41 61 302 89 18