Hybrid Ecological Footprint of Taipei

Abstract

The Ecological Footprint (EF) has been effectively used at the global, national and regional levels, but the local EF accounting methods are lacking. The hybrid EF has been developed to calculate the local EF. It combines a “top-down” approach to determining national EF (five components other than Carbon Footprint, CF) with a “bottom-up” approach to determining local CF (food, housing, transportation). The use of the hybrid EF is cost-effective. The hybrid EF reflects the local context and can be used to measure the progress of local sustainable development and as a basis for environmental responsibility. This study uses statistical databases for Taiwan and Taipei to calculate the hybrid EF of Taipei in 2018. The hybrid EF of Taipei was 4.797 global hectares (gha) in that year, of which the top-down national EF was 0.613 gha and the bottom-up local CF was 4.184 gha. The hybrid EF is lower than Taiwan’s EF (6.460 gha), but the local CF is higher than Taiwan’s CF (3.890 gha), reflecting the urban nature and characteristics of Taipei, which has a high density, high income and high consumption expenditure. With respect to the local CF of Taipei, food is associated with the largest component of CF (2.806 gha), and transportation is associated with the second largest component thereof (1.133 gha). Housing is associated with the smallest component (0.245 gha). Based on these results, five refinements of hybrid EF accounting and two application dimensions are proposed. First, whether the hybrid EF captures the lifestyle of the real situation in Taipei warrants further investigation. Second, the components of national EF that are associated with food should be used to accommodate regional differences by applying a scaling factor. Third, Taiwan’s CF in 2018 accounted for 60.2% of its national EF, but Taipei’s CF accounted for 87.2% of its hybrid EF. Fourth, Taipei’s CF associated with housing is low (0.245 gha/person), while the values for eastern European cities are high (3.140 gha/person). Fifth, Taipei citizens have a fairly high CF associated with private vehicles, warranting a follow-up review of urban sustainable transportation policies.

1. Introduction

According to the 2020 global Living Planet Index, the number of monitored species of mammals, birds, amphibians, reptiles and fish declined by an average of 68% between 1970 and 2016 [1]. The genetic propensity for Homo sapiens to expand is being reinforced by socially constructed cultural memes and the neoliberal growth ethic, the most dramatic manifestation of which is global capitalism [2]. The Earth under global capitalism is required not only to accept more people, but also larger people [3]. The world is bound to a mythical, eternal material growth that is encouraged by technological progress [2], and even “exhaustible resources do not pose a fundamental problem” [4] (p. 205). According to the Living Planet Report 2020, the current developmental needs of human society are more than 1.56 times the regenerative capacity of the Earth. Changes in the world’s natural systems may have ended the improvements in human health and well-being that have been enjoyed in the past century, and nature has sent a distress signal to human beings [1], concerning the current situation and the changing utilization of natural capital. The structural stability and functional safety of natural ecosystems must be ensured to promote sustainable regional development.

Achieving the aforementioned goal has become a global concern [5,6]. Numerous studies have proposed many metrics of sustainability, such as the life cycle assessment [7], the human development index [8], the environmental performance index [9], Emergy [10], Exergy [11] and the Ecological Footprint (EF) [12]. The EF is considered to be a comprehensive, complete and effective indicator of the use of natural capital in a region [5,13,14,15,16,17,18]. A 2008 European Commission study even claimed that, despite the need to supplement sustainability indicators and further improve data and related methodologies, the EF is a useful indicator of the use of sustainable natural resources that is easy to communicate and understand [19].

The EF has been extensively used by the international community in recent years, and it is related to the new transformation of sustainable development from narrative discourses to accounting indicators, which directly or indirectly quantify the interrelationship among human society, the natural environment and ecosystem services. On the one hand, the EF helps us to understand the impact of human activities on the natural environment, and, on the other hand, it helps us to calculate the function of ecosystem services [14,16]. The EF subtly links human social systems to natural ecosystems using the unified benchmark of bioproductive land area [5,20]. The EF can transform complex statistical items into a clear value, specified in a single unit—global hectare (gha). Accordingly, it is a comprehensive environmental indicator of sustainability that can be tracked, compared and discussed in relation to other indicators on a long-term basis [15,17,21]. Hence, it enables researchers to track the use and overshoot of natural resources by human societies and understand the impact of human behavior on the environment [16].

The EF was first proposed in 1992 by William Rees [12], who identified six types of “bioproductive land” as the basis for the use of the EF as an indicator of sustainable development. It is sufficient to calculate the basic services of human activities without double-counting more than two types of services. After a rolling review over the recent years, the six types of bioproductive land have been identified as cropland, grazing land, forest land, fishing grounds, built-up land and carbon footprint [20]. The EF is calculated from the per capita consumption (Ci) of the above six consumption items (i), as calculated using statistical data. In order to convert the EF into a unified value, the per capita consumption of each item (Ci) is divided by the average productivity (Pi) of that item (i). The values thus obtained for the six items are summed to yield the per capita EF: EF (gha/person) = Ci/Pi.

The calculated EF must be modified for trade and global equivalent productivity. The trade modification reflects the consequences of the frequent trade exchanges in the global economy and the frequent import and export of consumer goods. Consumer goods are associated with various land productivity values; for example, cropland is highly intensively used and therefore has high productivity, whereas pasture is usually extensive and so has low productivity. Therefore, the “equivalent value modification” is required and data are processed using the “equivalent factor” (EQF) [14].

After the EF of the six types of bioproductive land are calculated, the consumption and supply of ecological resources can be determined using the basic formula for environmental sustainability. The utilization of ecological resources can thus be estimated. The basic formula is as follows [17,22].

BC (biocapacity, production and supply side) − EF (consumption and demand side) = ER (Ecological Reserve) (when the value is plus)

or

BC (biocapacity, production and supply side) − EF (consumption and demand side) = ED (Ecological Deficit) (when the value is minus)

when the BC exceeds the EF, an “ecological reserve” is said to exist; conversely, when the EF exceeds the BC, an “ecological deficit” is said to exist. The close tracking of these two indicators is called “ecological balance” [17]. The ED reflects the over-consumption of natural resources by humans: a larger deficit correspond to a greater overshoot. The world went from an ecological surplus to an ecological deficit around 1970 [15], meaning that since 1970, we have been consuming the natural resources of future generations and increasing environmental costs every year [14].

The EF and BC have been extensively computed at the global and national levels [14,15,18,23], and sub-national footprint analyses are beginning to be carried out at the regional level [8,24,25,26]. However, on the city scale, a holistic and integrated means of assessing urban environmental footprints is lacking [17,27]. Currently, carbon and water footprints are the most commonly used for evaluating urban environmental performance, although their range of application is limited. The concept of urban metabolism (UM) allows for the inventory of flows to and from cities, but it does not allow these flows to be interpreted in terms of environmental impact. Life cycle assessments (LCA) provide this capability, but they are not yet feasible on a city scale.

The local EF can be calculated in three ways; these are the top-down scaling methodology, the bottom-up questionnaire survey methodology and the hybrid approach (which combines the top-down and bottom-up methodologies). The hybrid approach effectively combines the advantages of the scaling methodology with those of the questionnaire survey methodology. Statistical data are used in calculation to reduce costs, but local data are generated to reflect local characteristics. Drawing on Świąder et al. [17], this study compares the hybrid EF to the conventional top-down EF. The hybrid EF combines the top-down approach of conventional EF and the bottom-up EF to identify the main flows at the macro level, as well as the main hotspots and potential improvements at the micro level [27]. This study considers Taipei, in Taiwan, as an example and thus explores the local EF of a high-density city in a subtropical industrialized economy.

2. Meaning and Content of the Local EF

2.1. The Meaning of Local EF

Rees argued that the impact of modern urban life on the environment must be absorbed by the biological resources provided by the environment outside the city [12]. He used this conclusion as a starting point in developing environmental sustainability indicators (including the EF). Paradoxically, most mainstream discussion of the EF has not focused on this urban/out-of-the-city idea [17,27,28]; rather, it has involved countries and regions. A considerable wealth of EF accounting at the national and regional levels is available. These are highly comparable, traceable and stable, mainly owing to the existence of the Global Footprint Network (GFN) [29] and national databases.

National EF databases measure the environmental impact of human behavior across the globe and within countries based on long-term tracking. They thus contribute to the sustainable environmental and energy policies of various nations. For example, COVID-19 was found to have significantly reduced national EFs in 2020. Global lockdowns have brought manufacturing to a standstill and drastically reduce the use of fossil-fuel-powered vehicles. Long-term air pollution in major manufacturing countries has dissipated [30]. Gonçalves [31] even claimed that COVID-19 is bad for people, but good for the planet. According to the GFN, the global EF fell sharply in 2020 due to the COVID-19 pandemic: the EF of forest lands declined by 8.4%; that of croplands was unchanged. The global carbon footprint (CF) declined by 14.5% [32].

EF data at the local level are lacking relative to those at the national level, weakening the effect of the EF on policy, and especially on environmental and sustainability-related policies within countries. The ecological deficit in urban areas is generally accepted to be much higher than that in rural areas. Local EFs would support the discussion of policies that recognize the differences between urban and rural areas, such as the imposition of a hometown environment tax.

Studies of EF accounting for cities have been published [17,27,33,34,35]. However, there is no consensus on the methodologies. The methodologies in these studies vary greatly [17] and lack cross-reference, mostly because EF accounting depends on the statistical database of each country. Stable and accurate data exist at the national scale, but those at the local level (such as cities) are fragmented, incomplete, and collected using various methods. Overcoming this challenge regarding the sources of statistical data is the main challenge for research into local EF accounting.

2.2. Local EF Accounting

The Local EF can be calculated in three ways; these are the top-down scaling methodology, the bottom-up questionnaire survey methodology, and the hybrid approach (which combines the top-down and bottom-up methodologies).

2.2.1. Top-Down Scaling Methodology

The scaling methodology is calculated proportionally to the consumption of a country and of a local area using the COICOP (Classification of Individual Consumption According to Purpose) consumption categories [36]. For example, meat consumption in Wroclaw, Poland, is 4.56 kg/person, and that in Poland as a whole is 5.32 kg/person. A scaling factor of 0.86 is obtained by dividing these two numbers. Factors for other items (such as vegetables, rice, clothing, hospital services and others) can be obtained similarly. If data are lacking, the scaling factor is assumed to be unity. Finally, the consumption of items is converted into areas of bioproductive lands. The EF of a country can be converted into the EF of a local area, using the scaling factor for individual items [17]. This conversion is a top-down scaling conversion. Its advantage is that as long as the relevant information is complete, the calculation is easily performed. However, it relies greatly on clearly classified statistical items, and capturing the differences in lifestyle and the effects of environmental policies among various areas is difficult.

2.2.2. Bottom-Up Questionnaire Survey Methodology

The questionnaire survey method targets the lifestyle of individuals. A structured questionnaire survey is provided to residents in a specific area, based on regional sampling, age sampling, random sampling and other forms of sampling. For example, Rashid et al. [35] used questionnaire surveys and an ecological calculator that was developed by Redefined Progress to calculate the EF of the Rawalpindi urbanized area of Pakistan. They found that the resources that were consumed in this area far exceeded the biocapacity of Pakistan. This approach, which reflects the population through sampling, has the advantage of capturing lifestyle differences and so provides quite detailed information. However, it is costly to implement.

2.2.3. Hybrid Approach

The hybrid approach effectively combines the advantages of the scaling methodology with those of the questionnaire survey methodology. Statistical data are used in calculation to reduce costs, but local data are generated to reflect local characteristics. In the hybrid approach, a part of the local EF is calculated separately and reflects local characteristics (bottom-up), whereas most of it is obtained from the national EF (top-down). The big difference is in the CF, owing to lifestyle variation [17]. On average, the CF accounts for about 60% of the national EF [17]. The CF is the most important component of the EF [37], and lifestyle differences from area to area have the greatest impact on the CF [38]. By so doing, the hybrid approach can be easily adopted to calculate the local EF for cities and counties in different settings and cultures.

2.3. Accounting for the Hybrid EF

EF Accounting (EFA) is a method for estimating the environmental carrying capacity. It can be used in combination with urban metabolism analysis (UMA) [39]. This combination requires the areas of productive land and water ecosystems that are required for urban metabolism to be calculated [40]. The EFA estimates the energy and material resources that are consumed by the city, as well as the biocapacity that is required to absorb its waste [12]. Using data that are generated by a bottom-up analysis of energy and resources, UMA and EFA can help local governments understand how urban metabolism in an area affects the demand for ecosystem services in that area [40].

The key to the development of local EFA is the elimination of the dependence on national data. The use of a top-down scaling approach at the local level enables comparisons with other countries or regions that conduct such assessments. However if the input/scaling data do not reflect the specific conditions of local context that are affected by environmental policies, then top-down assessments are likely to be less responsive than they otherwise would be to policy shifts. Therefore, they are supplemented using the hybrid approach [17,33]. The two main approaches to hybrid EFA are the component-based and compound-based approaches [41]. The former uses local data, while the latter uses national data.

2.3.1. Component Hybrid EFA

The component hybrid EFA, which combines EFA with UMA, can be regarded as an evolution of EF computational methods. It can be further divided into two sub-methods [42]. The first is the input-output analysis, which relies on national economic data and does not capture local energy and resource flows [40].

The second sub-method uses local data directly to calculate actual energy and resource consumption. Components of the EF that correspond to various sectors are obtained; these sectors are food, construction, transportation, water, consumer goods and waste. Components for all sectors are summed to derive the EF of the city [42]. The calculation has two parts; the first part concerns energy, resource consumption and waste, and the second concerns the area of the ecosystem that is required for the production of energy and resource flows.

2.3.2. Compound Hybrid EFA

The compound hybrid EFA appropriates some of the national data to determine part of the local EF. Therefore, the compound hybrid EFA combines the top-down national EF (some parts) with the bottom-up local CF to yield a local EF.

Which part of the national EF can be appropriated? Świąder et al. [17] believes that, on average, more than 60% of the national EF is CF, and other footprints do not vary substantially at the national level. Built-up land and forest land are developable and protected areas, respectively, and the other three types of land correspond to food footprints (cropland, grazing land, fishing ground). Thus, Świąder et al. [17] proposed that five types of bioproductive land (excluding CF) are appropriated from the national EF and distributed to the nations’ regions according to population. Local EFA requires the calculation of only the CF.

In essence, in the compound hybrid method (or the CF hybrid method), the local CF is calculated separately, capturing local characteristics (bottom-up), and it is combined with the other five components of national EF (top-down, scaling approach) to yield the hybrid local EF.

3. Taipei EF: A Compound Hybrid EF

This study considers Taipei, in Taiwan, which is a subtropical island state, as an example and determines this city’s compound hybrid EF. Taipei, located in northern Taiwan, is a basin municipality with high-density development, an area of 271.8 km² and a population density of 9287.70 person/km², which is the highest in Taiwan. The mean annual temperature is 23.5 °C, so air conditioning is extensively used in summer, and heating equipment is rarely used in winter. Moreover, owing to the high population density of Taiwan, the country has the highest motorcycle density in the world. Taipei is the region with the highest density of cars and motorcycles in Taiwan [43]. Therefore, the local EFs under these specific conditions warrant study [27].

The compound hybrid EF combines the “top-down” national EF with the “bottom-up” local CF [17,40]. The determination of the “top-down” national EF involves the use of a national statistical database to calculate the EFs of five types of bioproductivity land (cropland, grazing land, forest land, fishing ground and built-up land), followed by the conversion of the EFs using the relevant scaling factors. The bottom-up CF is obtained by calculating local CFs according to the lifestyles in various areas, using local statistical databases. The advantage of the compound hybrid approach to EFA is that it can reduce research costs, capture local characteristics and be used to evaluate the effectiveness of sustainable development policies.

Of the five “top-down” EFs (associated with cropland, grazing land, forest land, fishing ground and built-up land), those associated with cropland, grazing land and fishing ground, related to the bottom-up food CF, should be excluded from the top-down EFA to avoid double accounting. The “top-down” national EF is an EF of only forest land and built-up land. After the EFs of forest land and built-up land are added, a scaling factor that reflects the lifestyle in the area should be applied. This scaling factor relates the average local household consumption expenditure to the average national household consumption expenditure.

The bottom-up local CF is taken from Świąder et al. [17] and divided into three categories, which are CF associated with food (food consumption and food waste), CF associated with housing (water use, electricity use, natural gas use, waste disposal, wastewater treatment) and CF associated with transportation (public transportation and private transportation). All the CF-related data used herein were for 2018. The Equivalence Factor (EQF) for forest land in 2018 (approximately 1.29) was the basis for the conversion of local value to global value (gha), and the global CO₂ sequestration rate (IsCO₂) (0.72) was taken from Mancini et al. [37]. In this study, the CF of all items was calculated, and the EQF and CO₂ sequestration rate were multiplied (EQF × IsCO₂ = 1.29 × 0.72 = 0.9288) to yield the final gha.

Table 1. Comparison between National and Hybrid Local EFs.

National EF (Conventional EF)
Items	Top-down National EF
	Cropland	Grazing Land	Fishing Ground	Forest Land	Built-up Land	CF
Local EF (Compound Hybrid EF)
Items	Top-down national EF					Bottom-up local CF
	Cropland	Grazing land	Fishing ground	Forest land	Built-up land	CF
	Incorporated into the bottom-up CF of food *			Only these two EFs are accounted		Food	Housing	Transportation

* See the calculation method in Section 3.2.1. “CF Associated with Food”.

compares national and hybrid local EFs.

Table 1 shows that, unlike the national EF (which only needs to add up the six types of biodiversity land), the local EF includes top-down national EF and bottom-up local CF. The top-down national EF only calculates the forest land and built-up land (the cropland, grazing land and fishing land are incorporated into the bottom-up CF of food). The bottom-up local CF calculates food CF, housing CF and transportation CF. After calculating the top-down national EF and bottom-up local CF, we then add these two values to obtain the compound hybrid EF for the local area.

3.1. Taipei’s Top-Down EF

The data for the top-down EF are from the national database in Taiwan, and the data for the bottom-up CF are from Taipei’s statistical database, which will be defined accordingly in the following sections. The Committee of Agriculture (COA) and the Chung-hua Institution for Economic Research (CIER) in Taiwan have been conducting research into the EF. Their studies of the EF refer to the research into, and the development and transformation of, the EF by international organizations, such as GFN [15,20,37]. They have applied the latest research methods to calculate and review Taiwan’s EF for years [14].

According to Lee et al. [14], Taiwan’s EF was 7.690 gha/person in 2000, and then gradually decreased to 6.460 gha/person in 2018 (

Table 2. Taiwan’s EF from 2000 to 2018 (unit: gha/person).

	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
EF	7.69	6.91	7.20	7.31	6.20	6.85	6.63	6.73	6.35	6.19	6.73	6.70	6.61	6.30	6.48	6.47	6.40	6.51	6.46

Source: [14].

). In 2018, the EF associated with cropland was 1.810 gha/person, that associated with grazing land was 0.120 gha, that associated with fishing ground was 0.180 gha, that associated with forest land was 0.270 gha, that associated with built-up land was 0.190 gha, and the CF was 3.890 gha (

Table 3. Taiwan’s EF in 2018 (unit: gha/person).

Item	Taiwan’s EF
gha	6.460
Sub-item	Cropland	Grazing land	Fishing ground	Forest land	Built-up land	CF
gha	1.810	0.120	0.180	0.270	0.190	3.890

Source: [14].

). The CF accounted for 60.2% of the total EF, a ratio that is similar to the global average (60%), reflecting the fact that Taiwan’s carbon emissions and CF must be reduced in line with the global reduction of carbon emissions to reach the goal of net-zero emissions in 2050.

The EFs of Taipei exclude those top-down EFs that are associated with cropland, grazing land and fishing ground, which are included in the CF associated with food. Only the top-down EFs associated with forest land and built-up land were calculated and included. The EFs associated with forest land and built-up land added up to 0.460 gha/person (Table 3). To reflect lifestyles in Taipei, this value should be multiplied by the scaling factor to obtain the top-down conventional EF of Taipei.

The scaling factor is calculated proportionally to the consumption of a country and of a local area using the COICOP consumption categories [36]. This study calculates the proportion of the household consumption expenditure of Taipei to that of Taiwan to obtain the scaling factor for Taipei. Household consumption includes all expenses related to meeting the needs of family members. The source of household consumption is the disposable income of all the income of family members, which accounts for about 60% of the country’s gross domestic product (GDP) and is thus an important variable in the analysis of economic demands [44]. If the household consumption expenditure is high, the food consumption will also be high. Therefore, calculating the proportion of the household consumption expenditure of Taipei to that of Taiwan can obtain the scaling factor for Taipei, which is similar to the scaling factor using the COICOP consumption categories.

According to the 2018 Report on the Survey of Family Income and Expenditure [45], the average household consumption expenditure in Taiwan was NT$0.811 million, and that in Taipei was NT$1.083 million, which is the highest in Taiwan. A scaling factor can be obtained from the average household consumption expenditure per household in Taipei divided by that in Taiwan (1.083/0.811 = 1.333). Hence, the top-down conventional EF of Taipei is 0.460 × 1.333 = 0.613 gha/person (

Table 4. Conventional EF of Taipei (Forest Land and Built-up Land) (unit: gha/person).

	Conventional EF		Scaling Factor (SF)	Conventional EF’ = Conventional EF × SF
Items	Forest land	Built-up land	1.333	0.613
	0.270	0.190
	0.460

).

3.2. Taipei’s Bottom-Up CF

3.2.1. CF Associated with Food

The CF associated with food comprises CFs associated with food consumption and food waste, calculated as the number of people in the area * the weight (kg) of food consumed per capita. However, data on food consumption by weight in Taipei are lacking, and although a considerable amount of food waste is generated there, Taiwan’s unique recycling culture and derivative food waste recycling technology yield a food waste recovery rate of almost 100%, so the relevant calculation should be adjusted accordingly.

With respect to consumption, according to the “Taipei City Household Income and Expenditure Accounting Survey Report” [46], the consumption of food is calculated on the basis of COICPO [36], which is given in NT$—not weight. With respect to supply, wholesale markets such as the Taipei Poultry Wholesale Market, the Taipei Fruit and Vegetable Market and the Taipei Huannan Market record relevant weights sold, but their data concern trading markets in all of Northern Taiwan, and so are not necessarily associated with Taipei alone.

In the absence of data on the weight of food consumed, the regional food items are converted to local CF using the relevant scaling factor. First, the national EF that is associated with food includes components associated with cropland (1.810 gha), grazing land (0.120 gha) and fishing ground (0.180 gha), which add up to 2.110 gha/person; this is then multiplied by the scaling factor 1.333. The CF that is associated with food for Taipei is therefore 2.813 gha/person (=2.110 × 1.333).

Waste disposal in the analysis of Świąder et al. included the disposal of food waste [17]. According to the Taipei Environmental Protection Bureau [47], 65,285 tons of kitchen waste was recycled in Taipei in 2018, corresponding to an average of 179 tons per day, of which pig kitchen waste was 12 tons (7%) and compost kitchen waste was 167 tons (93%). Nearly 100% of the food waste was recycled and reused. Pig kitchen waste was distributed to pig farms, and compost kitchen waste was processed and converted into solid compost, liquid fertilizer and soil conditioners. The recycling of food waste increases the productivity of bioproductive lands. Therefore, food waste should not be included in the CF associated with food.

3.2.2. CF Associated with Housing

The CF associated with housing consists of five components, which are electricity use, water use, natural gas use, wastewater treatment and garbage disposal.

• CF associated with electricity use

The CF associated with electricity use is calculated by multiplying the population in an area by the electricity use per capita to yield the total electricity use; this is then multiplied by the CO₂ emission index of electricity to yield the tonnage of CO₂ that is associated with power consumption. According to the “CO₂ Emission Index for Electricity”, published on the website of the Bureau of Energy, Ministry of Economic Affairs (MOEA), the generation of electricity produced about 0.521 tons of CO₂ in 2018 [48]. According to the Taipei Municipal Accounting Office [49], Taipei consumed a total of 16,193.21 million kWh of electricity in 2018, corresponding to an average of about 2857 kWh of electricity per person, yielding a total CF of 39,221 gha and a per capita CF of about 0.015 gha.

• CF associated with water use

The CF associated with water use is calculated by multiplying the total volume of water supplied by the electricity that is consumed by all technologies, such as reservoirs, waterways and water purification systems that are required per unit volume of water supply, and then converting the result into a volume of CO₂ emissions. According to the tap water statistics of the Water Resources Agency, MOEA [50], the per capita daily water consumption in Taipei in 2018 was 332 L. The water supply technology was divided into two major systems—reservoir water storage and tap water supply. According to Sinotech Engineering Consultants [51], the carbon emissions associated with reservoir storage include those associated with reservoir construction, sewage pumping and sludge removal, reservoir maintenance and so on. These were calculated to be between 0.0012 and 0.0208 kg of CO₂ equivalent per cubic meter of water. The value of “total electricity consumption + total fuel consumption/total water supply” based on relevant data from the Water Company was 0.156 kg of CO₂ emissions per ton of water supplied. Approximately 0.001768 tons of carbon were emitted per ton of water supply. A CF of 557,040.2244 gha was obtained by multiplying the population of Taipei (2.64 million) by the per capita daily water consumption (332 L) by 365 days by the carbon emissions per ton of water supplied (0.001768). The per capita of CF associated with water use was therefore 0.241 gha.

• CF associated with natural gas use

The CF associated with natural gas use was calculated by multiplying the total population of Taipei by the volume of gas used per capita and then by the calorific value (expressed in calories per unit volume) of LNG. According to the Taipei Municipal Accounting Office [49], about 80% of households in Taipei used natural gas in 2018, and 20% used barreled gas. Owing to a lack of exact sale figures for barreled gas, the per capita calorific value of barreled gas used was considered to be the same as that of natural gas used. According to the Taipei Municipal Accounting Office [49], the annual consumption of natural gas in Taipei in 2018 was approximately 338,425 cubic meters. The energy density conversion was based on a unit calorific value table of energy products that was provided by the Bureau of Energy, MOEA [48]. The calorific value of natural gas per cubic meter was about 27.76 GJ/m³. The CF associated with natural gas use in Taipei was calculated to be 11,743.3 gha, and the per capita value was 0.0045 gha.

• CF associated with wastewater treatment

The CF associated with wastewater treatment was calculated, as was that associated with electricity use. The total volume of wastewater treated was multiplied by the CO₂ emission index of electricity. In 2018, the total volume of wastewater collected in Taipei was 295 million cubic meters [49], and the wastewater treatment rate was 82%. The final secondary treatment volume was 201 million cubic meters. According to Huang et al. [52], the energy consumption index of a wastewater treatment plant is given in units of kWh/m³ (electrical energy consumed per unit volume of wastewater treated), kWh/kgBOD5 or kWh/kgCOD (the electrical energy consumed per unit mass of pollutant BOD5 or COD). If kWh/m³ is used as the unit of power consumption in wastewater treatment, then, based on the relevant data, the average wastewater treatment plant in Japan in 1999 consumed about 0.26 kWh/m³; that in the US consumed about 0.2 kWh/m³; such a plant in Germany in the year 2000 consumed 0.32 kWh/m³. The average of these three values, 0.26 kWh/m³, was used herein for wastewater treatment plants. Wastewater treatment in Taipei was calculated to have a CF of 27,593.28 gha and a per capita CF of 0.01060 gha.

• CF associated with garbage disposal

The CF associated with garbage disposal, as calculated by Świąder et al. [17], can be used for Taipei if kitchen waste is properly subtracted. The relevant data for Taiwan are quite detailed, with the weight of garbage already deducted from the weight of kitchen waste. Therefore, the mass of disposed garbage was used and converted to generate CO₂ emissions. The three garbage treatment plants in Taipei had treatment capacities in 2018 of Neihu—12,123 tons, Beitou—40,196 tons and Muzha—20,660 tons, for a total of 72,979 tons [49].

Taiwan Watch [53] estimated energy consumption in terms of embodied energy from the extraction of raw materials to the final product. In 2015, household garbage comprised 13.78% plastic, 0.43% leather, 38.64% paper, 2.08% glass, 0.85% metal; the rest of the waste was mostly man-made products. Accordingly, the embodied energy of each kilogram of household garbage is about 28.62 MJ (million joules). Therefore, 72,979 tons of garbage in a year is equivalent to 579.31 million kWh of electricity. This value corresponds to a CF of 1402 gha and a per capita CF associated with garbage disposal of 0.00053 gha.

3.2.3. CF Associated with Transportation

Transportation comprises public transportation and private transportation; public transportation is further subdivided into bus and rail transportation. Rail transportation in Taipei includes Mass Rapid Transit (MRT), Taiwan Railway and Taiwan High-speed Rail (THSR). The services extend to the Greater Taipei area, and calculating actual transportation mileage is difficult. According to Su [54], rail transportation can be divided into MRT and intercity railway. Only the CF associated with MRT was calculated herein. The MRT is identified as the main form of public rail transportation, and the Taiwan Railway and THSR are intercity railways, which were not considered in the calculation of the CF. With respect to private transportation, Taiwan is a motorcycle power-house, with more than 10 million motorcycles. The motorcycle is one of the main modes of transportation in the Greater Taipei area, and so the calculation of CF considers them along with passenger cars and buses.

• CF associated with public transportation

The CF associated with public transportation is calculated by multiplying the CF per kilometer of relevant vehicles by the total annual mileage traveled therein. According to the Department of Transportation, Taipei City Government, the total mileage of buses in 2018 was 175,603,933 km, and that of the MRT in 2018 was 22,941,921 km [55]. According to Świąder et al. [17], buses emit 1.6 kg of CO₂ per kilometer, and the MRT emits 10.1 kg of CO₂ per km. The CF associated with public transportation in Taipei in 2018 was calculated to be 512,679.6949 gha, and the per capita value was 0.19719 gha.

• CF associated with private transportation

The CF associated with private transportation was calculated by multiplying the total number of vehicles by their average annual fuel consumption and converting the total fuel consumption to CF, based on the fuel (gasoline, diesel, electric/hybrid). The calculations of Świąder et al. [17] did not consider motorcycles. However, Taipei has approximately 900,000 motorcycles and 800,000 passenger and freight vehicles. The huge number of motorcycles cannot be ignored, so motorcycles were considered in the calculation, as were passenger trucks. In 2018, the popularity of electric/hybrid vehicles grew significantly, with 10,000 hybrid motorcycles and 20,000 hybrid passenger vans on the road. According to the Urban Cycling Institute, electric vehicles have a similar energy efficiency to that of gasoline vehicles [56], so they are included in the calculation of the CF associated with gasoline vehicles.

More than 90%, about 860,000, of the 900,000 motorcycles were ordinary gasoline motorcycles [55]. The calculation assumed that all were 125 cc motorcycles. The annual fuel consumption was obtained from the “Energy Efficiency Table” that was provided by Sanyang Motorcycle Company [57]. A total of 14 models of 125 cc motorcycles were considered; they had various values of fuel consumption (indicated by classes 1–5, where class 1 corresponds to low fuel consumption and high efficiency, and 5 corresponds to high fuel consumption and low efficiency). The average fuel efficiency of new and old motorcycles of different factory years was captured by assuming an average annual fuel consumption of 95 L. The total number of gasoline motorcycles was 924,482, and the total number of electric motorcycles was 11,776, yielding a total consumption of 88,944,510 L of gasoline.

According to the Department of Transportation, Taipei City Government [55], about 90% of small vehicles used gasoline in 2018 and 80% of large vehicles used diesel; so, in this study, small passenger vehicles were assumed to use gasoline and large passenger vehicles to use diesel. Electric/hybrid vehicles were included in the number of vehicles in gasoline vehicles. The total number of gasoline vehicles (including electric vehicles) was 721,615, and the total number of diesel vehicles was 86,511. According to Świąder et al. [17], the annual fuel consumption of gasoline vehicles is 1200 L, and the annual fuel consumption of diesel vehicles is 1050 L. Gasoline vehicles thus consume 865,938,000 L of gasoline per year, and diesel vehicles consume 90,836,550 L of diesel per year.

In summary, Taipei’s private vehicles consumed 931,562,910 L of gasoline per year (the gasoline to MJ factor was 32.2 and the corresponding CO₂ emission factor was 73.1) and 90,836,550 L of diesel (the diesel to MJ factor was 35.9 and the corresponding CO₂ emission factor was 73.1). Multiplying the liters of gasoline (diesel) used by the gasoline (diesel) to MJ conversion factor and multiplying by the relevant CO₂ factor yields the total CF. The CF was 2,434,048 gha, and the per capita CF was 0.936 gha.

3.2.4. Summary

In summary, the bottom-up CF of Taipei was associated with three categories: food, housing and transportation. The per capita CF associated with food was 2.806 gha. The five components of the CF associated with housing related specifically to electricity use (0.015 gha), water use (0.214 gha), natural gas use (0.005 gha), wastewater treatment (0.011 gha) and garbage disposal (0.001 gha). The two components of transportation were public transportation (0.197 gha) and private transportation (0.936 gha). The three categories of CF together yielded a hybrid bottom-up CF of 4.184 gha/person for Taipei (

Table 5. Hybrid CF Accounting for Taipei (unit: gha/person).

Items	Food	Housing					Transportation		Sum
EF	2.806	0.245					1.133		4.184
Items	CF of food	Electricity use	Water use	Natural gas use	Wastewater treatment	Garbage treatment	Public transportation	Private vehicles
EF	2.806	0.015	0.214	0.005	0.011	0.001	0.197	0.936

).

The per capita hybrid CF of Taipei was 4.184 gha; the component of the CF associated with food was the largest, at 2.806 gha per person; it was followed by that associated with transportation, at 1.133 gha per person; the CF associated with housing was 0.245 gha per person. The per capita disposable income in Taipei is the highest in Taiwan, and the scaling factor of “household income and expenditure” was 1.333. Applying the scaling factor for Taiwan’s traditional EFs associated with cropland, grazing land and fishing ground revealed that the CF associated with food is larger than that associated with the other two categories (housing and transportation). Secondly, the CF associated with private transportation was higher than that associated with public transportation. In 2018, more than 800 million liters of gasoline were sold in Taipei. Transit-oriented Development (TOD), which has been advocated by the Taipei City Government for many years, must be actively pursued. Water use and wastewater treatment associated with housing are responsible for considerable emissions of CO₂, reflecting the fact that Taipei has the highest per capita water consumption in Taiwan, and therefore a relatively lower electricity use than other places in the country. Perhaps, since water supply, wastewater treatment, garbage disposal and other items were converted into equivalent electricity generation to calculate the CF, the component of CF that is associated with actual electricity use might have in fact been lower than indicated.

3.3. Taipei’s EF

This study proposes that the EF of Taipei is a combination of the “top-down” national EF (forest land and built-up land: 0.613 gha) and the “bottom-up” local CF (food, housing, transportation: 4.184 gha). The sum of these two values yields a hybrid EF of 4.797 gha for Taipei (

Table 6. Taipei’s Hybrid EF (unit: gha/person).

	National EF (Forest Land + Built-Up Land)	Local CF (Food, Housing, Transportation)	Hybrid EF
EF	0.613	4.184	4.797

).

From

Table 7. EFs of Taiwan and Taipei in 2018.

	Taiwan	Taipei
EF (gha/person)	6.460	4.797
CF (gha/person)	3.890	4.184
CF/EF (%)	60.2	87.2

, Taiwan’s national EF in 2018 was 6.460 gha/person, of which the CF was 3.890 gha, or about 60.2%; this proportion is close to the global average. The hybrid EF of Taipei was 4.797 gha/person, which was lower than Taiwan’s average traditional EF, and inconsistent with the relevant studies. Świąder et al. [17] found that the hybrid EFs of Eastern European cities exceeded the traditional EFs of the corresponding countries; this finding warrants further exploration. The CF of Taipei was 4.184 gha, or about 87.2% of the hybrid EF of the city; this proportion is much higher than the traditional EF, perhaps because Taipei is a high-density, high-income, high-consumption expenditure city, so its CF (especially that part associated with food) was much higher than its traditional EF—a finding also worthy of further exploration.

4. Conclusions and Suggestions

People who are looking for a bright side to the COVID-19 crisis tend to highlight its positive ecological outcomes, such as reduced pollution, traffic, consumption and production [29,32,58]. However, these outcomes offer no reason to celebrate, as the planet needs a sustained reduction of long-term pollution; specifically, greenhouse gas emissions must be reduced by 7.6% per year till 2030 [31]. If the COVID-19 pandemic has taught us one thing, it has taught us about the existence of a deep, undeniable link between the health of the planet and the global economic system. The difficulty of reaching a global political consensus on the importance of acting quickly, the restrictions that must be imposed and the way to offset their effects economically and socially is now clearer than ever.

The COVID-19 pandemic is a feedback signal from the biosphere [59], indicating the overshoot of the human species (currently estimated to be about 1.75 Earths) [29]. As long as an overshoot persists, efforts to control the pandemic, even if successful, will have failed to address the broader overshoot problem and the prospect of further, more threatening signals or “transition events” (including partial collapses of the biosphere) [60]. If policy-makers target the “Corona Challenge” but not the overshoot [32] and our overburdening of the planet, we will be exposing ourselves to another such feedback signal, and another one after that, until we take appropriate global action to reduce our impact on the biosphere. Each such signal will be more severe than the previous one and cause much human and non-human suffering [2].

To determine whether humans have exceeded the limits imposed on us by the availability of ecological resources, numerous studies have used the EF to draw global, national and regional inferences, but few have considered the local level. Based on the findings of Moore et al. [40] and Świąder et al. [17], this study developed a method for calculating a hybrid, local EF for Taipei, which is located in northern Taiwan, a subtropical island state. Five points concerning this hybrid EF, which combines the “top-down” national EF and the “bottom-up” local CF, warrant reflection.

First, the purpose of the EF is to determine quantitatively whether human activities cause overshoot, and the purpose of local EF accounting is to determine whether the lifestyle in an area favors or disfavors overshoot. Accordingly, a part of the conventional EF is converted into the local EF using a scaling factor for “household income and expenditure”. The hybrid EF of Taipei was 4.797 gha/person, of which the CF was 4.184 gha. These values were compared with the conventional EF and CF (6.460 gha and 3.890 gha, respectively; Table 7): the hybrid EF was smaller but the hybrid CF was larger. Whether the hybrid local EF captures the lifestyle of the real situation in Taipei warrants further investigation. For example, what scaling factor should be used to yield a more accurate local CF?

Second, owing to the lack of statistical data concerning the local CFs associated with food, the local CFs associated with the three types of land related to food production (cropland, grazing land, forest land) were appropriated from the national EF and then converted to local CFs using the aforementioned scaling factor for the income and expenditure of each household. Does this approach double-count the component of EF associated with food items? Should an appropriate subtraction be made? In this study, since the national EFs associated with food were directly appropriated to estimate the local CFs associated with food, if the components of food were added to the national EF, then it would be double-calculated. Furthermore, Taiwan’s food self-sufficiency rate is low, and cross-border transactions and north-south exchanges in Taiwan are frequent. Accordingly, the EF associated with food is difficult to assign to a particular location. The lifestyles of cities and counties are quite different—a fact that is reflected in differences in household income and expenditure. Therefore, in the absence of statistical data related to the CF associated with food, the components of national EF that are associated with food should be used to accommodate regional differences by applying a scaling factor to avoid double-counting. Furthermore, the recycling rate of food waste in Taipei is nearly 100%, unlike in European and American cities, so no component of CF associated with food waste has to be calculated.

Third, Taiwan’s CF in 2018 accounted for about 60.2% of its national EF, but Taipei’s CF accounted for about 87.2% of its hybrid EF; this proportion is much higher than for Taiwan and higher than values found elsewhere [17,61,62]. For example, in the case of the top-down assessment for Wrocław, Poland, the total CF represented 63% of its national EF, while Wrocław’s CF accounted for about 73% of its hybrid EF [17]. The reason for this difference may be that Taipei is a high-density, high-income, high-consumption expenditure urbanized area, and the calculation of the CF associated with food involves a scaling factor for household income and expenditure. Taipei’s household consumption and expenditure in 2018 was 1.333 times the average in Taiwan (and the highest in Taiwan), so the CF component of Taipei’s hybrid EF was relatively high.

Fourth, Taipei’s CF associated with housing is low (0.245 gha/person), while the values for eastern European cities are as high as 3.140 gha/person—approximately ten times higher. We posit two major reasons. The first is the climatic difference related to latitude, which affects the need for heating. Heating equipment consumes considerable electricity and natural gas. Eastern Europeans need heat to survive cold winters, but high-energy heating equipment is seldom used in Taiwan. On the contrary, air conditioning is needed in Taiwan to reduce indoor temperatures in the summer. However, the overall CF associated with electricity use in Taipei was only 0.015 gha/person in 2018—a fact worthy of further examination in follow-up research in similar settings. The second reason can be framed as a question: if the EF of Taipei is low, then which counties or cities in Taiwan have a high EF? According to data from the Taiwan Power Company, more than 70% of electricity is not used for domestic purposes, and this proportion is growing steadily. The high electricity consumption of industrial counties and cities may be the main source of their high CF, which is worthy of thorough elucidation in future studies and in similar settings/cultures.

Fifth, the CF associated with public transportation (0.197 gha) in Taipei is less than that associated with private transportation (0.936 gha). Although Taipei’s MRT provides high-density transportation and the TOD policy has been strongly championed, automobiles and motorcycles are still the densest form of transportation in Taiwan. Taipei’s citizens have a fairly high CF associated with private vehicles, warranting a follow-up review of urban sustainable transportation policies. In South East Asian cities, motorcycles play a critical role in the urban transportation system [63,64], and so the way to apply Taipei’s case study to other similar settings in that region also warrants follow-up studies. In the pandemic era, maintaining safe physical distancing while reducing the use of private vehicles represents a major challenge in the planning and design of sustainable cities.

Two dimensions of the application of the hybrid EF are worth exploring. First, although some critics oppose the use of biocapacity to calculate ecological deficits or overshoots [8], an appropriate EF analysis of ecological deficits and overshoots warrants further development in other settings/cultures. Second, in addition to calculating the local EF, an analysis of the local EF could be combined with a measurement of human development to assess whether a city is on a sustainable developmental trajectory. For example, in addition to determining whether the per capita EF is lower than the available per capita biocapacity, whether the EF and the Human Development Index can be decoupled [26] can be analyzed with a view to having a positive impact on the socio-economic level without damaging the environment.