Citywide Energy-Related CO₂ Emissions and Sustainability Assessment of the Development of Low-Carbon Policy in Chiang Mai, Thailand

Abstract

Cities are one of the key contributors to the environment and sustainability. This study aims to quantify citywide energy-related CO₂ emissions and assess the sustainability feasibility of implementing climate change mitigation policies in Chiang Mai, Thailand. By employing the GPC method, it was found that the average energy-related CO₂ emission in Chiang Mai from 2015 to 2019 was 2,146,060 tCO₂eq. Residences, industries (i.e., food preservation industries), and commercial and governmental buildings were the top three energy consumption-related GHG emitters. According to the Analytical Hierarchy Process (AHP), in terms of mitigation measures, LED lighting presented the highest score (0.380), followed by improving air conditioning efficiency (0.278), and the use of energy-efficient appliances (0.203). Energy-efficient technologies would be more feasible than the development of renewable energy technologies to lower CO₂ emissions. In terms of sustainability, political, technical, and economic feasibility criteria presented the highest AHP score (0.789), followed by human and social dimensions criteria (0.129), and environmental performance criteria (0.073). Policy possibility had the highest AHP score, while direct contribution to climate benefits as GHG reduction presented the lowest score. The integration of climate mitigation opportunities into national policies, the green industry scheme, and promoting residents’ self-determined motivation are urgently recommended.

1. Introduction

The global human population is increasing exponentially. According to the Population Reference Bureau [1], the global population is projected to increase from 7.7 billion in 2020 to 9.9 billion by 2050. This dramatic growth is clearly associated with inevitable urban growth. It is projected that the world will have 43 so-called megacities, with more than 10 million inhabitants, by 2030, most of which will be in developing regions [2]. Although cities have been the dominant driving force for economic growth and development, increasing population density in cities leads to risks and challenges for both humans and the environment. The rise of megacities in the global economy has boosted the demand for both primary and secondary raw materials. It can lead to greater pressures on land and other finite natural resources, including energy, food, and mineral resources. In terms of energy consumption, over two-thirds of the global primary energy consumption was attributable to cities. Furthermore, cities account for more than 75–80% of global greenhouse gas (GHG) emissions [3]. The International Energy Agency [4] estimated that energy-related GHGs in the urban areas accounted for about 71% of the total emissions in 2008 and this number is expected to rise to 76% by 2030. More specifically, energy consumed in the building sector accounts for as much as 30–40% of the global energy demand [5]. From a consumption-oriented point of view, approximately 80% of C40 cities, which is a network of global megacities committed to addressing climate change, contributed the largest share of consumption-based GHG emissions [3]. It should be noted that urban form and technological and economic factors were considered major determinants of citywide GHG emissions. VandeWeghe and Kennedy [6] estimated residential- and transport-related GHG emissions in the Toronto Census Metropolitan Area to define the impacts of urban form on emission-causing activities and found that GHGs from private auto use sources are on par with those from fuel use for building heating. Dhakal [7] found that the 35 largest cities in China accounted for about 40% of the total energy usage and carbon emissions. By employing the input–output subsystem model, Ge et al. [8] investigated the sectoral roles in GHG emissions in China and found that the three-largest CO₂ emitters were electricity and heat production and distribution; transportation, storage, postal services and telecommunications; and metal mining. In terms of the final sectoral demand, construction was considered the largest carbon emitter. Specifically, the service sectors generated more carbon emissions than the manufacturing and agricultural sectors. CO₂ was the largest component (99.45%) of the total emissions, followed by N₂O (0.45%) and CH₄ (0.10%), respectively.

Above all, it is often emphasized that cities are at the forefront of reducing total urban energy consumption and cutting carbon emissions. A study by Tian et al. [9] revealed that citywide energy-related CO₂ emissions have gained increasing attention in recent years because citywide GHG inventories can promote energy efficiency and climate change mitigation policy, and thus help guide the direction of a city’s emission reduction efforts. From a policy point of view, Thailand intends to cut its GHG emissions by 20% from the business-as-usual (BAU) level by 2030 as mentioned in its Intended Nationally Determined Contribution (INDC) [10]. Correspondingly, Thailand’s INDC was formulated based on the National Economic and Social Development Plans, Climate Change Master Plan B.E. 2558–2593 (2015–2050), Power Development Plan B.E. 2558–2579 (2015–2036), Thailand Smart Grid Development Master Plan B.E. 2558– 2579 (2015–2036), Energy Efficiency Development Plan (EEDP) B.E. 2558–2579 (2015–2036), Alternative Energy Development Plan (AEDP) B.E. 2558–2579 (2015- 2036), Environmentally Sustainable Transport System Plan B.E. 2556–2573 (2013–2030), National Industrial Development Master Plan B.E. 2555–2574 (2012–2031), and Waste Management Roadmap. As indicated in the National Climate Change Master Plan, emissions from energy and transport sectors would be reduced by 20–70% by 2020 compared to emissions under the BAU scenario. By 2050, the proportion of investment in low-carbon and environmentally friendly industries would be increased, while the ratio of GHG emissions to GDP would be reduced. To achieve the INDC targets, the EEDP aims to lower energy intensity by about 30% in 2036 compared to that in 2010 primarily through (i) compulsory measures (i.e., enforcement of energy conservation standards in designed factories and buildings, building energy code in new buildings, energy labeling on equipment/appliances (HEPS and MEPS), and the Energy Efficiency Resource Standard) and (ii) voluntary measures (i.e., promoting increased use of LEDs via price mechanisms, supporting financial incentives, and supporting energy performance achievements and energy-saving measures in the transport sector) [11].

To foster sustainable urban development and limit the net carbon emissions, it is very important for cities to better understand and credibly measure their own baseline greenhouse gas emissions. Indicatively, cities must play a central role in global climate change mitigation and the implementation of low emission development strategies (LEDS) [12]. Many countries are promoting the implementation of climate change-related policies focusing on the deployment of low-carbon power and energy-efficient technologies across all sectors. A sustainability assessment of measures related to energy and climate change mitigation is urgently needed. Recently, Rösch et al. [13] assessed the sustainability of the German energy system by considering the following goals of sustainable development: (i) securing human existence (i.e., protection of human health, satisfaction of basic needs, autonomous subsistence based on income from own work, and just distribution of opportunities to use natural resources), (ii) maintaining society’s productive potential (i.e., sustainable use of renewable and non-renewable resources, sustainable use of the environment as a sink for waste and emissions, such as energy-related GHG emissions, sustainable development of human and knowledge capital, such as added value creation from energy efficiency measures in households), and (iii) preserving society’s options for development and action such as participation in societal decision-making processes and society’s capability for self-organization. Despite the importance of the topic, there is a total lack of databases on energy-related CO₂ emissions at the provincial level in developing countries like Thailand. Further, the local implementation of climate change mitigation actions faces several challenges, including a lack of alignment of climate policies, institutional blockage, and low prioritization of motivation for climate policy adaptation [14].

Therefore, this study estimated citywide energy-related CO₂ emissions and proposed some potential climate change mitigation measures in the energy sector. A multi-criteria decision analysis based on the Analytical Hierarchy Process (AHP) and sustainability indicators were employed to assess the feasibility of implementing energy-efficient and energy-saving measures to lower CO₂ emissions in Chiang Mai, Thailand, from a sustainability perspective. Ultimately, the results of this research can provide deeper insights into how city-scale actions can potentially contribute to both the global climate goals and the NDC targets. Furthermore, this research will contribute to the existing body of knowledge because it is one of the first studies conducted in a country that applied sustainability indicators and a multiple-criteria decision analysis approach to the investigation of the energy sector’s potential in driving low-carbon strategies and related policies at a city-wide scale. As a regional economic and socio-cultural hub in the northern part of Thailand, Chiang Mai was selected as a case study for this research.

2. Materials and Methods

2.1. Case Study

As a regional economic hub of the north and the second-largest province of Thailand, Chiang Mai province was selected as a case study. The city is located approximately 700 km (435 miles) north of Bangkok (Figure 1). In terms of urbanization, many parts of the province have undergone rapid land use changes, especially during the rapid urban expansion and population growth. In 2020, the population and Gross Provincial Product (GPP) of Chiang Mai were 1,779,254 and 135,785 million Thai Baht (THB), respectively. Non-agricultural sectors, including industry and service, contributed about 81.3% of total GPP in 2018 [15]. Electricity consumption by both the service and industry sectors has grown substantially. As one of the most popular tourist attractions in the country, Chiang Mai received 3.2 million overseas tourists and about 7.5 million domestic visitors in 2018.

2.2. Boundaries of Energy-Related CO₂ Emissions

The Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) was employed to estimate citywide energy-related CO₂ emissions [16]. In this context, the GPC indicated three scopes to calculate and report city-scale GHG emissions. Scope 1 covers direct emissions from city-owned sources that are located within the city boundary, Scope 2 covers indirect emissions associated with purchased electricity, heat, and steam within the city boundary, and Scope 3 covers all other indirect emissions occurring outside the city boundary. It should be noted that only emissions under Scope 1 and 2 were reported in this study and expressed as CO₂ equivalent (CO₂eq). The geographic boundary of Chiang Mai province served as the boundary for the city’s GHG inventory.

2.3. Estimation of Energy-Related CO₂ Emissions

As previously mentioned, emissions under both Scope 1 (i.e., all emissions from fuel combustion) and Scope 2 (i.e., all emissions from the use of grid-supplied electricity within the city boundary) were accounted for and estimated. The energy consumption data of each sector were collected from the Chiang Mai Provincial Electricity Authority (PEA) and Provincial Industry Office. Furthermore, based on the GPC, a scaled-down method using population indicators was used to represent the overall citywide energy consumption in this research. CO₂ emissions from the energy used by the city were calculated using Equation (1). Stationary energy emissions mainly originate from residential buildings, commercial and institutional buildings, and the manufacturing and industrial sectors. Energy use in agriculture and forestry was also included.

$$Energy GHG emissions\left({\mathrm{CO}}_2\mathrm{eq}\right)={\displaystyle \sum }Energy use{d}_{i,j}\times E{F}_{i,j}$$

(1)

where Energy GHG emissions is total energy-related CO₂ emissions (CO₂ equivalent), Energy used is the total electricity consumed (kilowatt-hours; kWh) and each type (i, j) of fuel (liters) in the city, and EF is the Emissions Factor of each type of energy (i, j).

2.4. Potential Options for Mitigating Citywide GHG Emissions

2.4.1. GHG Mitigation Potential in 2030

Regarding GHG mitigation scenarios, both the business-as-usual (BAU) scenario and the Nationally Determined Contributions (NDCs) mitigation plan were projected. CO₂ emissions in 2015, which was the base year, were estimated and forecasted to the target year, 2030, in the BAU scenario, which assumes no additional climate change or introduction of energy policies. In the NDC scenario, the following policy interventions and measures indicated in Thailand’s NDC are accounted for: (i) energy-saving strategies (i.e., installing LED lights and using more efficient heating and cooling systems, energy-efficient appliances, and high-efficiency cooking stoves) and (ii) applying renewable energy (i.e., solar power and biogas energy). Chiang Mai’s provincial economic growth rate was estimated at 4% to forecast trends in GHG emissions between 2015 to 2030 using Equation (2).

$$Economic growth rate (\%)={\left(\frac{GP{P}_{current yr}}{GP{P}_{first yr}}\right)}^{1/\left(n-1\right)}-1$$

(2)

where GPP_{current yr} is the Gross Provincial Product in the present year, GPP_{first year} is the Gross Provincial Product in the first year of considered period and n is a number of the year intervals in the considered data.

2.4.2. Multi-Criteria Assessment of Climate Mitigation Policy in the Energy Sector

The Analytical Hierarchy Process (AHP), which is a Multi-Criteria Decision Making (MCDM) technique, was employed to help prioritize climate change mitigation options and explore the key areas of concern for developing low-carbon policies for the energy sector (Figure 2). The prioritization of mitigation options using the AHP involves the following two steps.

Step 1: Selection of mitigation measures in the energy sector as indicated in Thailand’s NDC. In this context, the following options were proposed: (i) LED lighting installation, (ii) development and implementation of high-efficiency air conditioning systems, energy-efficient appliances, high-efficiency cooking stoves, and (iii) development of renewable solar cells and biogas energy.

Step 2: Weighting the relative priority of the proposed climate mitigation policies. An AHP pairwise comparison technique was performed based on expert interviews (n = 4) with representatives from Chiangmai Provincial Energy Office, Provincial Electricity Authority, Provincial Industry Office, and the Small and Medium Enterprises (SME) Support and Rescue Center of Chiangmai. The weighting of each mitigation option derived from Step 1 was computed using Equation (3). The experts have to provide a 9-point numerical scale in the pairwise comparison matrix from 1 to 1/9 [17]. All pairwise comparison numerical values were consequently normalized and summed to 1. To avoid any incidental judgment, the consistency ratio (CR) value was calculated. Theoretically, the estimated weighting coefficients are acceptable if the CR is less than 0.1 (Equations (4) and (5)).

$$A=\left[a_{ij}\right]=\left\{\begin{array}{ccc}1& a_{ij}\dots & a1n\\ 1/a_{ij}& 1& a2n\\ 1/a1n& 1/a2n& 1\end{array}\right\}$$

(3)

where A = [a_ij] is a representation of the expert’s preference for each GHG mitigation measure and defined as the element of row i and column j of the matrix (i,j = 1, 2, …, n)

$$CI=\frac{{\mathsf{\lambda }}_{\max }-n}{n-1}$$

(4)

where λ_max is the greatest eigenvalue of the pairwise comparison matrix and n is the factor number

$$CR=\frac{CI}{RI}$$

(5)

where CI is the consistency index and RI is the random consistency index

A modified sustainability assessment method [14,18] was employed to assess the sustainability of adopting low-carbon policies in the energy sector and implementing the most preferred climate policy measures derived via the AHP technique. As presented in

Table 1. Indicators for assessing the sustainability of developing low-carbon policies and the most preferred climate mitigation measures.

Aspects of Sustainability	Indicators	References
Environmental performance	• Direct contribution to environmental benefits	[13]
	• Direct contribution to climate benefits as GHG reduction	[13,18]
Political, technical and economic feasibility	• Costs and benefits of implementing climate mitigation measures	[13]
	• Policy possibility/stringency for non-compliance	[13]
	• Technological feasibility of implementing climate mitigation measures	[13]
Human and social dimensions	• Added value creation from energy efficiency measures in households	[13]
	• Participation in societal decision-making processes	[13,18]
	• Society’s ability of self-organization	[13]

, all environmental, technological, and socio-political aspects were designed to assess the sustainability of proposing climate mitigation policies in Chiang Mai, Thailand.

3. Results

3.1. Total Citywide Energy-Related CO₂ Emissions

Using the GPC method, the average energy-related CO₂ emissions in Chiang Mai in 2015 and 2019 was determined to be 2,146,060 tCO₂eq (maximum = 2,270,460 in 2019 and minimum = 2,042,584 in 2015) (Figure 3a). In 2015, under the BAU scenario, the residential building sector was the largest contributor to citywide energy-related CO₂ emissions (650,983 tCO₂eq or 31.87% of the total CO₂ emission) (Figure 3b). This result is supported by a previous study [19] that reported that residential buildings were considered an important source of GHG emissions and represent approximately 20% of the total energy consumption in the US. Interestingly, in this study, industrial energy consumption was considered the second largest CO₂ emitter, accounting for 27.69% of the total emissions (565,597 tCO₂eq). More specifically, as depicted in Figure 4a, food preservation and ice production industries were by far the largest contributor of GHG emissions attributable to industrial energy consumption in Chiang Mai (27–33%). Meanwhile, fish and seafood preservation and rice milling factories accounted for only 6–8% of the total emissions from industrial energy consumption. Similarly, food-related production accounted for roughly 29% of all consumption-derived GHG emissions in the European Union [20]. It could be presumed that electricity utilized for food production processes (i.e., used for operating cooling and freezing equipment) is one of the largest sources of energy-related CO₂ emissions. For instance, the climate impact of seafood factories is dominantly due to GHG emissions from onboard cooling equipment and diesel combustion [20]. In this research, the remainder of citywide energy-related CO₂ emissions in Chiang Mai were from commercial and governmental buildings (15.10%; 308,393 tCO₂eq), energy use in agriculture activities, and unidentified activities (Figure 3b). As the third-largest emitter of citywide energy-related CO₂ emissions, the primary sources of GHG emissions from commercial and governmental buildings in Chiang Mai were shopping malls (51.7%), universities and hospitals (32%), and hotel and apartment services (16.3%) (Figure 4b).

3.2. GHG Mitigation Scenarios

As mentioned earlier, the following two scenarios were developed to assess the potential of GHG mitigation in the energy sector of Chiang Mai province: (i) BAU and (ii) NDCs mitigation plan. In the BAU scenario, Chiang Mai’s total GHG emission is expected to increase from 2,042,583 tCO₂eq in 2015 to 3,248,243 tCO₂eq in 2030 assuming the forecasted annual economic growth rate of 4.0% (Figure 5). In 2015, GHG emissions from energy consumption in residential buildings and manufacturing and industrial sectors contributed the highest fractions of the total emissions at 31.87% (650,983 tCO₂eq) and 27.69% (565,597 tCO₂eq), respectively. This was followed by GHG emissions from energy usage in commercial and institutional buildings (493,425 tCO₂eq), which accounted for 24.16% of the total GHG emissions in 2015. Under the BAU condition, GHG emissions from residential sectors (approximately 1,211,942 tCO₂eq) are expected to contribute 37.31% of the total GHG emissions in 2030. This is followed by the manufacturing sector, which is estimated to emit around 1,087,147 tCO₂eq or 33.47% of the total. Emissions associated with energy demand in commercial and institutional buildings are projected to be around 742,273 tCO₂eq, which is approximately 22.86% of the total predicted GHG emissions in 2030. Potential GHG mitigation options were proposed based primarily on Thailand’s NDC.

More specifically, if all policy interventions as indicated in Thailand’s NDC Roadmap on Climate Change Mitigation (2021–2030) are “fully” implemented in Chiang Mai, the total GHG emissions would be reduced by approximately 189,378 tCO₂e or 5.83% of the total expected GHG emissions in 2030.

3.3. Sustainability Assessment of GHG Mitigation Scenarios and Mitigation Policy-Based AHP

The potential of mitigating energy-related GHG emissions from the energy sector and the feasibility of implementing climate change mitigation policies in Chiang Mai were assessed based on expert interviews and using the AHP-pairwise comparison technique.

Table 2. Overall AHP-weighting of energy-related GHG mitigation measures (CR = 0.0927).

Energy-Related GHG Mitigation Measures	Weight	Ranking
LED lighting	0.380	1
High-efficiency air conditioning system	0.278	2
Energy-efficient appliances	0.203	3
Solar cell power	0.072	4
Biogas energy	0.042	5
High-efficiency cooking stoves	0.026	6

shows the calculated AHP weighted scores of energy-related GHG mitigation measures in Chiang Mai’s energy sector. The results revealed that LED lighting presented the highest score (0.380) in the AHP pairwise comparison, followed by improving the energy efficiency of air conditioners (0.278), and the use of energy-efficient appliances (0.203) in both residential and industrial sectors. As implementing LED lighting demonstrated the greatest potential for climate mitigation, the behavior of Thai consumers in terms of purchasing energy-saving lighting products was investigated based on the Theory of Planned Behavior (TPB) [21]. It was found that attitude is a strong predictor of purchase intention towards LED lighting products, while the subjective norm remains the weakest predictor of purchase intention. In this research, improving the energy efficiency of cooking stoves showed the lowest score in the pairwise comparison (0.026). Among renewable energy technologies, solar power and biogas energy ranked 4th and 5th, respectively. These results imply that the implementation of energy-efficient technologies or energy-saving options would be more feasible than developing renewable energy technologies for lowering energy-related CO₂ emissions.

Apart from the multicriteria decision AHP analysis, sustainability indicators were used to assess the feasibility of implementing GHG mitigation scenarios and climate mitigation policies in the energy sector of Chiang Mai, Thailand. As presented in

Table 3. Overall AHP-weighting of the feasibility of climate policy implementation in the energy sector (CR = 0.070).

Sustainability Aspects	Factors	Weight	Ranking
Environmental performance (0.073)	• Direct contribution to environmental benefits	0.042	6
	• Direct contribution to climate benefits as GHG reduction	0.030	8
Political, technical, and economic feasibility (0.798)	• Costs and benefits of implementing climate mitigation measures	0.230	3
	• Policy possibility	0.322	1
	• Technological feasibility of implementing climate mitigation measures	0.247	2
Human and social dimensions (0.129)	• Added value creation from energy efficiency measures in households	0.036	7
	• Participation in societal decision-making processes	0.045	5
	• Society’s ability of self-organization	0.048	4

and Figure 6, political, technical, and economic feasibility criteria showed the highest AHP score (0.789). Policy possibility presented the highest score compared to all other sustainability indicators (0.322), followed by the technological feasibility of implementing climate mitigation measures (0.247) and costs and benefits of implementing climate mitigation measures (0.230). Although the possibility of climate change policy development showed the highest score in the AHP pairwise comparison, the effectiveness of city-level policy (i.e., climate change and energy saving) mainly depends on the ability of local policies to meet GHG reduction goals while pursuing both economic growth and fiscal sustainability [22]. This is one of the possible reasons why the costs and benefits of mitigation measure implementation ranked third among all AHP factors. Overall, criteria with human and social dimensions showed the second-highest score (0.129), including society’s ability for self-organization in the implementation of energy-efficient technologies or energy-saving options (0.048) and participation in societal decision-making processes on climate change energy policies (0.045). Interviews with experts revealed that a lack of or ineffective multi-stakeholder participation (i.e., local residents and representatives from industries and private sectors) is a major obstacle in the drive for climate change mitigation policies and other initiatives in their community. Altogether, environmental performance criteria showed the lowest score in the AHP comparison (0.073) (i.e., direct contribution to both environmental benefits (0.042)). More surprisingly, direct contribution to climate benefits as GHG reduction and added value creation from energy efficiency measures in households showed the lowest AHP score compared to all other sustainability indicators in this study. Added value creation focuses on the creation of sustainable added value by promoting energy efficiency and energy saving in residential and household sectors. Rösch et al. [13] suggested that the sustainable development of man-made and knowledge capital to drive the sustainable use of energy resources in the community is urgently needed.

4. Discussion

4.1. CO₂ Mitigation Potential

As elaborated in the previous section, in a case where all of Thailand’s NDC mitigation measures are fully implemented in the energy sector, the total emissions in Chiang Mai would be lowered by 189,378 tCO₂e (5.83%). In support of this, a 5.83% GHG reduction by 2030 from energy-related CO₂ emissions alone was estimated in the BAU scenario. Previous studies have also primarily focused on energy efficiency improvement and the integration of energy generated with renewable resources. For instance, research conducted by Gouldson et al. [12] found that the most effective options for reducing carbon emissions in Kolkata, India, were embracing green building standards in all new buildings in commercial areas and implementing the most energy-efficient air conditioners in the residential sector. In Romania, a study carried out by Prada et al. [23] proposed intelligent energy efficiency solutions in hospital buildings, aiming to contribute to the 2050 target of 70% GHG emissions reduction, 70% renewable energy development, and 70% energy efficiency in buildings, under the new “70-70-70” efficiency concept. Further, a study performed by Bungău et al. [24] reported that energy requirement, energy performance class, and CO₂ emissions were considered to be key considerations in the assessment of sustainable living spaces. In Palembang City, Indonesia, the five most carbon-effective options were replacing diesel with biodiesel in industries, substituting diesel boilers with solar energy water heaters, promoting landfill gas waste to energy (WTE) utilization, promoting WTE, particularly in heat and power projects, and supporting energy efficiency in industries through steam reforming technology. In China, considering an annual GDP growth rate of 6.45%, the total primary energy demand is projected to increase by approximately 63.4%, 48.8%, and 12.2% in the BAU, carbon reduction, and integrated low-carbon economy scenarios, respectively [25]. Total carbon emissions will decrease by approximately 19.6% and 42.9% by 2050 in the carbon reduction and integrated low-carbon economy scenarios, respectively, in the BAU scenario. Zhou et al. [25] promote the use of all climate mitigation policies such as long-term low-carbon development strategies, improvement of energy efficiency, and development of economic instruments (i.e., carbon taxation). In Thailand, this is further supported by Misila et al. [26] who determined that the adoption of energy efficiency measures and the promotion of cleaner technologies, such as energy efficiency labeling, building energy codes, designated buildings, financial incentives, LED lighting, and renewable energy would lead to a reduction in GHG. Mitigation measures in the household sector are energy efficiency labeling, LED, and the adoption of renewable energy technology. Minimizing overall GHG and energy intensity and promoting energy diversification were considered as the co-benefits of the energy-related CO₂ reduction.

Furthermore, the Thailand Greenhouse Gas Management Organisation (TGO) [27] conducted a few studies on GHG emissions projections at the city level in Thailand and found that CO₂ emissions in some provinces are projected to reduce by about 1.01–18.23% by 2030 compared to the BAU baseline scenario. The ability of local governments to help mitigate GHG emissions and achieve climate commitments at the city level depends on local policies on climate change mitigation and low-carbon innovations. In this particular situation, the government should provide both technical and financial support to local authorities to foster a rapid transition towards a low-carbon society at the city level. For Chiang Mai, the integration of local climate actions within the AEDP, EEP, and the NDCs national plans should be more fully considered and fully implemented for long-term low-emission development strategies.

4.2. Climate Change Mitigation Policy Based on Multicriteria Decision AHP Analysis

Regarding the AHP-pairwise comparison, the results of this study are in line with a study conducted by Heinrich et al. [18]. This study reported that indicators of implementation feasibility such as administrative and financial feasibility and network capacity were the most important criteria for initiating GHG mitigation measures in the energy sector. In this previous study, climate benefits as CO₂ mitigation showed the highest score in AHP analysis, which is inconsistent with the current research. However, in the Asian Context, cities alone lack the capacity to continually drive climate change mitigation policies. In other words, city-level action on climate change associated with GHG emissions is partly determined by national initiatives on policies, strategies, and mechanisms. A multi-level cross-sectoral governance arrangement in climate change policy is, therefore, critically important.

4.3. Technical and Policy Implications

Through our analysis, it seems obvious that cities are in the frontline of the impacts of a changing climate and are essential for achieving sustainability goals. The following technical and policy recommendations were provided to promote and accelerate sustainable CO₂ emission reduction in the energy sector:

Environmental Sustainability: As mentioned above, environmental performance criteria (i.e., environmental benefits and climate benefit as GHG mitigation) had the lowest score in the sustainability assessment. According to Thailand’s Second National Communication under the United Nations Framework Convention on Climate Change conducted by the Office of Natural Resources and Environmental Policy and Planning, human resource and technical limitations in developing GHG inventory and climate change mitigation scenarios are key constraints in promoting low and zero-carbon solutions at the sub-national level [28]. It should be highlighted that the integration of climate change mitigation opportunities, particularly with energy as the priority sector, into sub-national policies, plans, and development projects in Thailand is urgently needed. More specifically, the feasibility of implementing technical measures (High Energy Performance Standard (HEPS), Economic Building (EB), and Zero Energy Building (ZEB)) for reducing energy-related CO₂ emissions in commercial buildings should be studied from a holistic perspective. Moreover, GHG emission reduction targets should be linked with the green industry scheme. Achieving higher sustainability in production systems in industries (i.e., food industry) is urgently needed. A combination of Life Cycle Assessment and Life Cycle Cost (LCC) analysis for energy saving and GHG mitigation measures in residential buildings should be also conducted in conjunction with the sustainability assessment scheme [29].

Human and Social Sustainability: Value creation through household energy efficiency and saving presented the lowest AHP score among the social sustainability criteria. This result is supported by Dubois et al. [30], who found that there was an urgent need to address the current mismatch between the roles conveyed by climate mitigation policies and households’ perceptions of their individual responsibility. In this context, based on the model of goal-directed behavior (MGB), Cheung et al. [31] reported that self-determined motivations and intrinsic motivation were important predictors of pro-environmental intentions and actions, particularly for household energy consumption behavior. In short, participants with high self-determined motivation are more likely to take pro-environmental actions. Therefore, improving residents’ self-determined motivation is considered the most effective and long-lasting solution to drive their willingness to adopt energy-saving measures and consequently mitigate GHG emissions [32]. Economic incentives as external rewards could effectively encourage people to adopt energy-saving behaviors and proactive climate actions in a comparatively short period of time. Local governments may consider cooperating with local authorities and electricity companies to offer financial rewards to residents instead of subsidizing residential electricity bills [31]. From a long-term sustainability perspective, both self-organizing behavior and participation in climate governance (i.e., planning and priority-setting) should be promoted by providing long-term environmental education to nurture environmentally conscious and aware residents. From a wider perspective, as hotels and apartment services were determined to be one of the largest sources of GHG emissions among commercial buildings in this study, sustainable tourism practices should be promoted and maintained in the long run. More importantly, to address stakeholder engagement strategies, this study strongly suggests that the Thai government should integrate climate change mitigation policies and related activities into the sub-national-level plan based mainly on decentralization processes. Cross-level interactions between national and sub-national governance levels and facilitation of multi-sector and multi-level climate change policy learning should be more organized. Moreover, to attract private sector engagement in climate change mitigation strategies, an ambitious GHG emission reduction target should be integrated into the green industry scheme.

5. Conclusions

Cities can play an important role in combating climate change. Based on the GPC and expert interviews and using the AHP-pairwise comparison technique, this study revealed that electricity consumption in residential buildings, manufacturing and industrial sectors, and commercial and governmental buildings were responsible for the largest share of city-level energy-related CO₂ emissions in Chiang Mai, Thailand. In terms of mitigation potential, if all policy interventions as indicated in Thailand’s NDC are fully implemented in the city, total GHG emissions would be reduced by 5.83% of the total GHG emissions expected in 2030 compared to the BAU scenario. In terms of sustainability, the AHP results showed that political, technical, and economic feasibility had the highest score, whereas environmental performance presented the lowest AHP score. Long-term environmental education and promotion of proactive behaviors and adaptive capacity through self-determined motivation are urgently needed to drive the transition to a low-carbon economy and sustainable urban communities.