- Email: [email protected]
Cleaner heating choices in northern rural China: Household factors and the dual substitution policy
Journal of Environmental Management 249 (2019) 109433
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Research article
Cleaner heating choices in northern rural China: Household factors and the dual substitution policy ⁎
Zhen Wanga,b, Cai Lib, , Can Cuib, Hui Liub, Bofeng Caic,
T
⁎⁎
a
College of Resource and Environment, Huazhong Agricultural University, Wuhan, 430072, China School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China c Center for Climate Change and Environmental Policy, Chinese Academy for Environmental Planning, Beijing, 100012, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Cleaner heating Dual substitution policy Environmental governance
Household heating is a major contributor to frequent winter haze in northern China. Transition to cleaner household heating is associated with multiple benefits including improved environmental conditions and health of local residents. This study presents an analysis of data from an indoor survey covering 1030 households in 136 villages of Hebi City in the winter of 2018. The main purpose of this study was to reveal the key factors that affect cleaner heating choices under the national pilot program of the dual substitution policy, which targets the replacement of coal heating with gas and electric heating. The survey showed that electric heating is the most popular heating method, and the adoption of cleaner heating rises with income, and energy and device costs may be the major barriers to adopting cleaner heating. Further, multinomial logit regression was used to investigate the household factors and found that income, heating area, energy cost, and education had significant impacts on heating choices. In addition, the gas substitution policy was more effective in promoting the cleaner heating transition than was the electric substitution policy. Results show that more freedom to choose energies and devices, as well as infrastructure for gas supply and centralized heating, is also vital for the adoption of cleaner heating. Our study provides new insights to improve the details of implementation of the dual substitution policy.
1. Introduction Winter heating has been recognized as a critical contributor to air pollution and cause of severe health effects in northern China (Dao et al., 2014). A study revealed that winter heating increased total suspended particulates in the ambient air by 55% in northern China and lowered life expectancy by 5.5 years (Chen et al., 2013). More recently, another study showed that winter heating increased PM10 concentrations by ~42 μg/m3 in northern China, causing a decrease in life expectancy by 3.1 years (Ebenstein et al., 2017). Due to the severe smog in northern China in recent years, air quality and respiratory health have become major topics of concern in the general public, which in turn have impelled the government to commit to improving air quality. Fundamentally, the unusual air quality in northern China is driven by excessive combustion of heating fuel. Therefore, the main strategy to improve winter air quality in the north is to change the heating method, which is currently based on coal burning (Cai and Jiang, 2008; Duan et al., 2014). Policies have an important impact on how household
⁎
energy is used. For example, implementation of the Coal Law has gradually eliminated the utilization of beehive coke ovens, contributing to the reduction of benzo(a)pyrene emissions in the ambient air and a 60% decrease in non-occupational lung cancer cases in China from 1982 to 2015 (Xu et al., 2018). Therefore, the central government of China launched a new policy in 2017, called the Cleaner Winter Heating Plan for the Northern Region (2017–2021), which will promote the use of electricity or natural gas instead of coal for winter heating and thus is often called the dual substitution policy. The goal of the dual substitution policy is to achieve a cleaner heating rate of 50% in 2019 and 70% in 2021, which will benefit the air quality of northern China (Zhao et al., 2018; Chen and Chen, 2019). Relatively speaking, in urban areas with central heating it is easier to achieve a cleaner transition under the dual effects of policy restrictions and financial subsidies, while decentralized household heating, especially in rural areas, usually involves a mixture of various energy and heating methods and thus has more complex circumstances during the energy transition (Zhu et al., 2018; Qin et al., 2018). Energy consumptions in rural households emit a large
Corresponding author. Corresponding author. E-mail addresses: [email protected] (C. Li), [email protected] (B. Cai).
⁎⁎
https://doi.org/10.1016/j.jenvman.2019.109433 Received 8 March 2019; Received in revised form 4 July 2019; Accepted 18 August 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
as well.
amount of pollutants outdoors and indoors (Chen et al., 2016; Jin et al., 2006; Tonooka et al., 2006). Therefore, to effectively achieve the dual substitution policy and improve winter air quality in the north, it is very important to investigate which factors influence the adoption of household heating methods in rural areas. Although cleaner energy and heating technologies are significantly valued by households (Scarpa and Willis, 2010), other barriers hinder the adoption of such cleaner technologies (Puzzolo et al., 2016). Recent studies have attempted to reveal the factors affecting the adoption of cleaner energies. For example, Cai and Jiang (2008) and Liu et al. (2013) both found that household size and income significantly influence the adoption of biomass heating in rural China, while Liu et al. (2013) also recognized that education is an important factor, and households with more educated members are more likely to adopt cleaner heating to reduce CO2 emissions. Duan et al. (2014) used nationwide survey data to investigate the heating patterns in rural and urban China and found that income per capita was a statistically significant factor affecting the adoption of energy types. Feng et al. (2011) also suggested that higher income related to diverse energy structures. Pachauri and Jiang (2008) argued that income, expenditure, cost, and location would affect the household energy transition. Similar results were also reported by Jiang and O'Neill (2004). Zhu et al. (2018) found that only liquefied petroleum gas adoption was associated with increased income, whereas income had no association with electricity adoption in rural China. Chen et al. (2006) evaluated energy choices in forest-poor and forest-rich regions in China and found that location and education affected the choice to substitute coal for fuelwood. zhang and Koji (2012) found that income was the key factor in rural energy consumption and rising energy prices have a strong negative impact on the use of energy. In addition to these studies in China, foreign studies have also revealed that factors have important impacts on household energy choices. Specifically, income and expenditures on household energy use were extensively explored (Ekholm et al., 2010; Mensah and Adu, 2015; Vaage, 2000). Mostly, high income and low cost would be more helpful to achieve the household energy transition. Age, family member, physical assets, education, and employment were also found has different impacts on household energy choice and consumption in Bhutan (Rahut et al., 2016a), Nigeria (Baiyegunhi and Hassan, 2014), Turkey (Özcan et al., 2013), India (Khandker et al., 2012) and many others. However, the aforementioned studies mainly discussed the energy structure, total or cooking energy choice, and/or its transition patterns (Alem et al., 2016; Rahut et al., 2016b). Table 1S in supporting information summarized the contents in detail for the existing literature which closely related to this study. There has been relatively less focus directly on heating choice at the household level in northern China or on assessing the effects of policy factors on heating choices. Therefore, a systematic survey and analysis of the factors influencing household adoption of cleaner heating are urgent and essential for advancing the implementation of the dual substitution policy. This study focuses on the rural household heating choice under the dual substitution policy in 2018, which is a timely study investigating the policy and its effects. In this study, an extensive household survey covering 1,030 rural households was conducted in Hebi City, in the Henan province of China, which is a pilot city for the dual substitution policy. Then, statistical analyses were used to determine the factors affecting the household adoption of cleaner heating. Results showed that electric heating is the most popular heating method, and the adoption of cleaner heating rises with income, and energy and device costs may be the major barriers to adopting cleaner heating. Income, heating area, energy cost, and education had significant impacts on heating choices. In addition, the gas substitution policy was more effective in promoting the cleaner heating transition than the electric substitution policy. To our best knowledge, this is the first study aiming to assess the effect of the dual substitution policy on rural household heating choice in northern China. New insights to improve the details of the implementation of the dual substitution policy have been provided
2. Methodology 2.1. Household survey The aim of the survey was to obtain a picture of the current status of cleaner winter heating in rural areas of northern China. The survey was conducted from 25 to 30 November 2018.1,030 households in 136 villages were indoor surveyed, accounting for 3‰ of total households and 18% of total villages in Hebi City, respectively. Our data covered all administrative districts above the township level in Hebi City, including three districts, two counties, and 40 townships. The questionnaire consisted of three sections and 55 questions. The basic information section surveyed the household information, including education level, household size, heating method, cleaner heating transition, the number of family members at home and the days of the members staying at home in the heating season at home, building status, and heating area. The policy information section surveyed the feedback, willingness to adopt, and willingness to pay for cleaner heating. The energy information section surveyed the household energy type, consumption, price, etc. All answers were collected in indoor face-to-face interviews by at least two trained investigators, most of whom were master's candidates from top-ranking universities in China. All answers were hand-filled and then digitized and double checked in Microsoft Excel. Detailed information about the questionnaires please refer to the supporting information. Due to the small numbers of some heating methods, all forms of electric heating (including electric blankets, fans, radiators, air conditioners, etc.) were categorized into a single group, namely electric heating. Central heating, geothermal, and other minor heating methods were categorized as other heating methods. Household factors were collected from the survey and, for simplicity and brevity, abbreviations were used to denote these factors as follows: household size (FM), heating area (AREA), heating energy cost (COST), middle-income household (INC3), high-income household (INC10), electricity substitution policy (ELE), gas substitution policy (GAS), junior school education (middle school), high school education (high school), higher education (Bachelor). Table 2S in supporting information gives the statistics for the above surveyed factors. 2.2. Statistical model In this study, multinomial logit regression was used to model the household heating adoptions. If the household adoption is y = 1, 2, …, J, where J is a positive integer, then there are j mutually exclusive heating choices. Obviously, given the condition x (household factors), J the sum of the probabilities of heating choices is 1: ∑ j = 1 P (yi = j|x i ) = 1. Therefore, multinomial logit regression is a natural extension of binomial logit regression to multi-choice selection. However, multinomial logit regression cannot estimate the coefficients of the influencing factors for all choices (βk, k = 1, 2, …, J) at the same time. The estimation must be based on a benchmark scheme. In comparison with the benchmark scheme, the probability of one household choose heating method j is 1
P (yi = j|x i ) = {
1 + ∑kJ = 2 exp (x′i βk ) exp (x′i βk ) 1 + ∑kJ = 2 exp (x′i βk )
(j = 1) (j = 2, ..., J )
(1)
The result of equation (1) can be obtained by maximum likelihood estimation, and its log likelihood function Li is
ln Li (βi, ..., βJ ) =
J
∑ j=1 1(yi = j) × ln P (yi = j|xi)
(2)
where 1(⋅) is an indicative function, the value of which is 1 if the 2
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
of other heating methods (see Fig. 2a), with a slightly lower frequency with lower costs (< 500 CNY) and a higher frequency with higher cost (> 500 CNY). Electric heating had the highest energy costs. However, the cost curve was still greatest at lower costs, with a peak frequency at 500 CNY, whereas the overall cost distribution was much broader than that of other heating methods, with relatively high frequencies at costs > 750 CNY. One possible reason is that the demand for energy varies greatly among different electric heaters. In general, small electric heaters, such as electric blankets and fans, consume less energy, whereas electric space heaters, such as electric radiators and air conditioners, consume more energy. From the survey, small heaters were the most frequently used electric heater. Fewer households adopted electric space heaters, and the energy cost was around 5 to 10 times (varied with the compared objects) higher than that of households using small heaters. In addition, the costs of auxiliary thermal insulation differed for different heating methods. The thermal insulation cost of other heating methods was still the lowest, followed in turn by coal heating, natural gas heating, and electric heating. However, the cost of energy might be endogenous to the cost of thermal insulation. For example, central heating in northern China is usually paid for according to the heating area. Under sufficient heating, the households have no incentive to invest in thermal insulation. Comparatively, households using small heaters may need to invest a large thermal insulation cost to maintain room temperature to overcome the cold during winter. The total household heating cost (energy + thermal insulation) is shown in Fig. 2b. It can be seen that the costs of electric and natural gas heating were obviously larger than those of the other two categories. In any case, other heating methods were the most economical. However, other heating methods required a large-scale investment in infrastructure with a long-term plan. Therefore, there is no option for ordinary households, especially rural households, to access other heating methods in the short term.
Fig. 1. Heating choices according to income level.
condition in parentheses is true, otherwise 0. By summing up the log likelihood functions of all samples, the coefficient estimations βˆk can be calculated by maximum likelihood estimation. The multinomial logit model assumes the independence of irrelevant alternatives (IIA), implying that removing a certain heating choice does not affect the consistency of the estimation of other choices but only reduces the estimation efficiency. In other words, in the case where the IIA null hypothesis cannot be rejected, there is no systematic difference between the coefficient estimations of the subsample after removing a certain choice and the coefficient estimation of the full sample. The Hausman test is often used to test whether a model violates the IIA assumption.
3.2. Multiple factors influencing household heating choice 3. Results and discussions Multinomial logit regression models were used to further reveal the impacts of household factors on heating choices. The survey results showed that a subset of other heating methods was used simultaneously with electric heating, coal heating, and natural gas heating, and the cost of other heating methods was much lower in each case. Therefore, the dual substitution policy may have a lesser impact on households that use other heating methods. If other heating methods are considered, the model will not converge (the choice is not exclusive, which violates the basic assumptions of logit regressions); thus, we excluded other heating methods in the multinomial logit models. In addition, most other heating methods require long-term infrastructure planning and construction, thus leaving no option for households to use these heating methods in the short term. Therefore, only the available heating choices were considered, including electric heating, coal heating, natural gas heating, and no heating. The sample size used in the multinomial logit models was 832. Table 1 shows the results of multinomial logit regressions, in which coal-fired heating was used as the benchmark. The Hausman test showed that after removing any of the choices, there was no systematic difference in the coefficient estimates of the subsamples. The regression results were authentic. Model 1 in Table 1 shows that household size (FM), energy costs (COST), income levels (INC3 and INC10), and education have significant effects on heating choice, and the signs of coefficients were in line with expectations. The probability of choosing electric heating was positively correlated with the number of family members, household income (the baseline is the low-income group), and education (the baseline is primary school). The survey answers implied that the highincome and high-education groups were more aware of the improvement in air quality associated with electric heating and were less sensitive to costs. The electricity substitution policy showed a positive but
3.1. Influence of income and cost on heating choice The income level determines the household's spending power and has a great impact on heating choice. The rates of household heating choices according to income group are shown in Fig. 1. In general, electric heating was the most popular heating method. The adoption rates were 48.8% in the low-income group (annual household income < 30,000 CNY), 65.3% in the middle-income group (30,000–100,000 CNY), and 65.2% in the high-income group (> 100,000 CNY). Coal-fired heating ranked as the second most popular choice, with adoption rates of 26.1%, 15.9%, and 8.0% in the three income groups, respectively. Approximately 3.9% of low-income households did not use any heaters throughout the winter, whereas the rates were only 0.9% and 0% in the middle-income and high-income groups, respectively. As household income increased, the rate of using cleaner heating increased significantly, whereas that of less-clean heating decreased (Fig. 1). For example, the rate of coal heating decreased significantly with income, whereas that of natural gas heating increased most with income, from 1.8% in the low-income group to 11.6% in the high-income group. One possible reason is that with increasing income, households have greater affordability and are willing to bear the increase in heating costs (the cost of cleaner heating is usually higher than that of coal heating) to obtain a better indoor environment (Richmond and Kaufmann, 2006). Heating costs are important determinants of household heating choice. Kernel curves for household heating energy costs are shown in Fig. 2. A cost curve closer to the y-axis in Fig. 2, with a higher kurtosis, indicates a lower cost. In general, other heating methods had the lowest energy costs. The energy costs of coal heating were very close to those 3
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Fig. 2. Kernel curves of household heating costs (kernel bandwidth 220).
method. Heating area is one of the main concerns in adopting gas heating. The sign of COST in Model 1 is not in line with expectations, probably because the gas subsidies reduced the heating costs. To verify this hypothesis, the interaction between GAS and COST were added in Model 2. The results showed that subsidies (proxied by GAS × COST) had a significant positive effect on gas heating choice. In addition, the sign of COST also changed as expected, indicating that the gas substitution policy has popularized gas heating mainly by reducing the cost of heating operation. The households that only use gas heating were selected to draw the kernel curves of the heating cost in Fig. 3. Subsidies reduced the unit-price of energy usage, they also prompt more energy use in the household which is known as the rebound effect (Liu et al., 2016). The combined effect is shown in Fig. 3a that implementation of the gas substitution policy has shifted the energy cost curve toward lower costs. That is, the blue line lies closer to the y-axis in Fig. 3a. The two line of the total cost (including energy use and
insignificant impact on electric heating choice, possibly because the policy variable was lack of variation. From the field survey, it is found that most households have adopted various forms of electric heating regardless of whether the region had implemented the electricity substitution policy. Surprisingly, the gas substitution policy had a significant positive effect on electric heating choice. A possible reason is that the substitution policy has strictly curbed coal heating and propelled the transition to cleaner heating. However, in most rural areas, pipeline gas is not available due to the lack of infrastructure. It is difficult for such households to reach a natural gas source. Therefore, the cleaner heating must shift to electric heating alternatives, which are much easier to procure than are other heating methods. For gas heating choice, the household members (FM), heating area (AREA), energy costs (COST), and gas substitution policy (GAS) had significant effects. Unlike most forms of electric heating, such as small heaters not affected by the heating area, gas heating is a space heating
Table 1 Factors affecting household heating choices. Model 1
FM AREA COST Income INC3 INC10 Policy ELE GAS
Model 2
Electricity
Gas
No heat
Electricity
Gas
No heat
0.122*** (3.54) 0.00771 (1.33) −0.00732*** (-4.30)
0.316*** (3.42) 0.0356*** (2.65) 0.00849* (1.84)
0.187 (1.29) 0.0690*** (2.79) −0.0411** (-2.34)
0.100*** (2.84) 0.00646 (1.10) −0.0189*** (-4.63)
0.284*** (3.00) 0.0336** (2.46) −0.00766 (-0.95)
0.212 (1.41) 0.0720*** (2.84) 0.0254 (0.24)
0.484*** (2.57) 0.856** (2.14)
−0.182 (-0.33) 0.645 (0.85)
−0.214 (-0.24) −12.03 (-0.01)
0.504*** (2.65) 0.930** (2.29)
−0.0765 (-0.14) 0.747 (0.97)
−0.293 (-0.33) −12.54 (-0.01)
0.245 (1.04) 1.088*** (4.91)
1.686 (1.55) 4.294*** (5.47)
13.73 (0.02) 0.0228 (0.02)
0.269 (1.13) 0.180 (0.54) 0.0946*** (3.19)
1.896* (2.12) 2.966*** (3.00) 0.0115*** (2.63)
14.06 (0.01) 1.444 (0.63) −0.0669 (-0.63)
2.222*** (2.92) 1.189*** (4.23) 0.786*** (4.11) −3.240*** (-4.22) 832 −516.732 0.1853 YES
1.955 (1.33) 1.131 (1.51) 1.018 (1.50) −17.26*** (-5.48)
−11.87 (-0.01) −12.84 (-0.02) 0.174 (0.20) −18.96 (-0.01)
2.192*** (2.88) 1.122*** (3.96) 0.784*** (4.08) −2.149** (-2.56) 832 −508.736 0.1979 YES
2.038 (1.37) 1.176 (1.55) 1.086 (1.57) −15.89*** (-4.92)
−12.50 (-0.01) −13.39 (-0.01) 0.0924 (0.11) −20.74 (-0.01)
GAS*COST Education Bachelor High school Middle school _cons N Log likelihood Pseudo R2 Hausman test
The values in parentheses are t-statistics. *p < 0.1; **p < 0.05; ***p < 0.01. 4
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Fig. 3. Impact of the gas substitution policy on household heating costs (kernel bandwidth 100).
residents' favorite devices and brands were not on the supply list, they hesitated to change heating methods. The energy option was also an important concern, as residents have their own preferences. From this, it can be seen that giving the residents more freedom to choose, rather than using a one-size-fits-all approach, might be more conductive to the implementation of the dual substitution policy. Lacking infrastructure, especially in rural areas, such as no pipeline gas or central heating, also obstructed implementation of the dual substitution policy.
thermal insulation; Fig. 3b) shows similar results. Namely, a lower kurtosis at lower costs and longer tails at higher costs (~15000 CNY) is shown after the implementation of gas substitution policy. But the two lines become much closer. It implies that, in the current policy environment, the high cost of purchasing heating devices and installation offset the effects of subsidies. Especially, device cost is usually high onetime investments, which may be an important factor hindering the adoption of gas heating. Compared with electric heating, the influence of income and education was not significant, indicating that cost was the decisive factor for adopting gas heating. Some of the low-income and middle-income households chose no heating in the winter. The models showed that heating area and heating costs were the major factors affecting the decisions of these households. The higher the heating costs and larger the heating area, these households were more likely to give up winter heating.
3.4. Challenges, policy implications and limitations The above results show that the key challenges in achieving the goal of cleaner air are the lack of energy infrastructure and policy flexibility. The severe smog in winter in northern China, energy structure reform is imperative. In large developed cities, central heating is the major method of winter heating. Through coal-to-gas technology remodeling, the cleaner heating renovation of most households in urban areas can be achieved in a short time. The scale effect of central heating brings another advantage in that better pollution-control technologies can be used in the heating systems to further reduce emissions. However, in small- and medium-sized cities, central heating infrastructure is lacking, and household-based heating and non-point source emissions occupy a dominant position. In our survey, households that can access central heating accounted for only 2.81% of the total survey. Therefore, at the current stage, establishing green and sustainable infrastructures, including clean energy supplies and centralized heating supplies, should be a priority in small- and medium-sized cities in northern China. Meanwhile, it is essential to strengthen the new urbanization strategy and encourage scattered rural residents to cluster in small- and medium-sized cities. Policy flexibility is also an important factor in achieving clean air goals. According to the survey, local residents hope to have more energy and device options. However. the current policy gives households less autonomy, resulting in the mismatch of the needs of residents and the supply of policies. Giving them the right to choose their own energy and devices helps the implementation of the policies. Therefore, for areas that cannot be provided with central heating, the dual substitution policy should be propagandized and implemented in a more flexible manner. Our results revealed the above challenges and proposed the following policy suggestions to address them: (1) the government should formulate more flexible rules to allow households to freely choose clean heating methods, devices, and brands and use postsubsidies to reduce households' heating and device costs, which are the major obstacles for low-income households adopting cleaner heating methods; (2) dispel the doubts held by households through better and proactive propaganda, making residents aware of the environmental and health benefits of cleaner heating, thus elevating the residents’ acceptance of cleaner heating; (3) revitalize the rural economy in various ways to increase the household income of rural residents, which will significantly reduce their sensitivity to heating costs and increase their willingness to adopt cleaner heating methods. Although our results revealed several important factors affecting a
3.3. Other policy implementation factors We surveyed which policy improvements promote a household's willingness to adopt cleaner heating methods by providing a −5 to 5 scoring system for answers, in which −5, 0, and 5 represented extremely disagree, neutral, and extremely agree with the proposed options in the questionnaire, respectively. The scores and error bars of major factors affecting the willingness to adopt cleaner heating are shown in Fig. 4. Operation subsidies and device subsidies ranked as the two top appeals, with average scores > 1, implying that reducing device and operation costs is still the most important factor in successfully achieving the dual substitution policy goals. The survey results were consistent with the results of the above empirical models. Currently, new devices are uniformly provided by government-approved suppliers. Suspicion of the device stability and the number of optional devices were other major concerns hindering adoption. When local
Fig. 4. Other factors affecting heating choice. 5
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Acknowledgement
household's heating choices, the conclusions still need further exploration. First, due to the short time span since the implementation of the dual substitution policy, there is a lack of continuous observation and survey data. While it is anticipated that factors such as heating area and cost will remain the most important factors in long-term observations, the roles of other household factors require more data to arrive at a robust conclusion. Secondly, although the total number of surveyed households was sufficient to characterize the heating choices of Hebi City, the data used to calculate the cost curve of a single heating method are limited, which may bias the cost comparisons between different heating methods. In summary, changes in household heating patterns in northern China under the dual substitution policy require more observation and research to systematically assess their impacts. Thirdly, our results show that the effect of the gas substitution policy functions better than the electric substitution policy, possibly because the cost of electric heating is higher than that of gas heating, and the existing forms of electric heating include a variety of methods from various small heaters to space heaters, which makes it more difficult for the government to implement heating subsidies to effectively reduce household heating costs. However, it is currently very difficult to collect and distinguish household-level subsidies for electric heating. The above hypothesis must be verified in future studies. Compared with previous studies, this study provides some new findings while the other main conclusions are consistent with previews studies. The major difference from the previous research results is mainly reflected in the rural energy structure. For example, Duan et al. (2014) found biomass was the dominant energy fuel for heating in 2012 in China and Zhu et al. (2018) found an increase of coal in heating during 1992–2012. In contrast, our study found electricity is the major energy form in Hebi city. The difference may be attributed to the different survey time and the rapid transition caused by the dual substitution policy. The transition was evidenced by the heating energy structure change in Beijing (Barrington-Leigh et al., 2019), which shows that policies have effectively changed the energy structure into electricity dominated one. Regarding the factors affecting the heating choices, our results show consistency with the research of Mensah and Adu (2015) in Ghana that income and education have a positive effect on cleaner energy adoption. Similar results were also found by Vaage (2000) in India that income, heating area, and family size were positive factors and by Scarpa and Willis (2010) that cost was a negative factor. These consistent results confirmed that household factors determined the effectiveness of the dual substitution policies. Therefore, policy improvement considering the aforementioned household concerns is essential.
This work is funded by the project entitled An Emission-TransportExposure Model Based Study on the Evaluation of the Environmental Impact of Carbon Market (No. 71673107) supported by the National Natural Science Foundation of China. The work is also financially supported by the National Key Research and Development Program of China (No.2018YFC0214001). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jenvman.2019.109433. References Alem, Y., Beyene, A.D., Köhlin, G., Mekonnen, A., 2016. Modeling household cooking fuel choice: a panel multinomial logit approach. Energy Econ. 59, 129–137. https://doi. org/10.1016/j.eneco.2016.06.025. Baiyegunhi, L.J.S., Hassan, M.B., 2014. Rural household fuel energy transition: evidence from Giwa LGA Kaduna state, Nigeria. Energy Sustain. Dev. 20, 30–35. https://doi. org/10.1016/j.esd.2014.02.003. Barrington-Leigh, C., Baumgartner, J., Carter, E., Robinson, B.E., Tao, S., Zhang, Y., 2019. An evaluation of air quality, home heating and well-being under Beijing's programme to eliminate household coal use. Nature Energy 4, 416–423. https://doi.org/10. 1038/s41560-019-0386-2. Cai, J., Jiang, Z., 2008. Changing of energy consumption patterns from rural households to urban households in China: an example from Shaanxi Province, China. Renew. Sustain. Energy Rev. 12, 1667–1680. https://doi.org/10.1016/j.rser.2007.03.002. Chen, H., Chen, W., 2019. Potential impact of shifting coal to gas and electricity for building sectors in 28 major northern cities of China. Appl. Energy 236, 1049–1061. Chen, L., Heerink, N., van den Berg, M., 2006. Energy consumption in rural China: a household model for three villages in Jiangxi Province. Ecol. Econ. 58, 407–420. https://doi.org/10.1016/j.ecolecon.2005.07.018. Chen, Y., Ebenstein, A., Greenstone, M., Li, H., 2013. Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy. Proc. Natl. Acad. Sci. U.S.A. 110, 12936–12941. https://doi.org/10.1073/pnas. 1300018110. Chen, Y., Shen, G., Liu, W., Du, W., Su, S., Duan, Y., Lin, N., Zhuo, S., Wang, X., Xing, B., Tao, S., 2016. Field measurement and estimate of gaseous and particle pollutant emissions from cooking and space heating processes in rural households, northern China. Atmos. Environ. 125, 265–271. https://doi.org/10.1016/j.atmosenv.2015.11. 032. Dao, X., Wang, Z., Lv, Y., Teng, E., Zhang, L., Wang, C., 2014. Chemical characteristics of water-soluble ions in particulate matter in three metropolitan areas in the north China plain. PLoS One 9, e113831. https://doi.org/10.1371/journal.pone.0113831. Duan, X., Jiang, Y., Wang, B., Zhao, X., Shen, G., Cao, S., Huang, N., Qian, Y., Chen, Y., Wang, L., 2014. Household fuel use for cooking and heating in China: results from the first Chinese environmental exposure-related human activity patterns survey (CEERHAPS). Appl. Energy 136, 692–703. https://doi.org/10.1016/j.apenergy.2014. 09.066. Ebenstein, A., Fan, M., Greenstone, M., He, G., Zhou, M., 2017. New evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River Policy. Proc. Natl. Acad. Sci. 114, 10384–10389. https://doi.org/10.1073/ pnas.1616784114. Ekholm, T., Krey, V., Pachauri, S., Riahi, K., 2010. Determinants of household energy consumption in India. Energy Policy 38, 5696–5707. https://doi.org/10.1016/j. enpol.2010.05.017. Feng, Z.-H., Zou, L.-L., Wei, Y.-M., 2011. The impact of household consumption on energy use and CO2 emissions in China. Energy 36, 656–670. https://doi.org/10.1016/j. energy.2010.09.049. Jiang, L., O'Neill, B.C., 2004. The energy transition in rural China. Int. J. Glob. Energy Issues 21, 2–26. https://doi.org/10.1504/IJGEI.2004.004691. Jin, Y., Ma, X., Chen, X., Cheng, Y., Baris, E., Ezzati, M., 2006. Exposure to indoor air pollution from household energy use in rural China: the interactions of technology, behavior, and knowledge in health risk management. Soc. Sci. Med. 62, 3161–3176. https://doi.org/10.1016/j.socscimed.2005.11.029. Khandker, S.R., Barnes, D.F., Samad, H.A., 2012. Are the energy poor also income poor? Evidence from India. Energy Policy 47, 1–12. https://doi.org/10.1016/j.enpol.2012. 02.028. Liu, J., Sun, X., Lu, Bin, Zhang, Y., Sun, R., 2016. The life cycle rebound effect of airconditioner consumption in China. Appl. Energy 184, 1026–1032. https://doi.org/ 10.1016/j.apenergy.2015.11.100. Liu, W., Spaargaren, G., Heerink, N., Mol, A.P.J., Wang, C., 2013. Energy consumption practices of rural households in north China Basic characteristics and potential for low carbon development. Energy Policy 55, 128–138. https://doi.org/10.1016/j. enpol.2012.11.031. Mensah, J.T., Adu, G., 2015. An empirical analysis of household energy choice in Ghana. Renew. Sustain. Energy Rev. 51, 1402–1411. https://doi.org/10.1016/j.rser.2015. 07.050. Özcan, K.M., Gülay, E., Üçdoğruk, Ş., 2013. Economic and demographic determinants of
4. Conclusion Winter heating is one of the main causes of haze in northern China. To reduce air pollution and its impacts on health, it is necessary to encourage local residents to adopt cleaner heating methods. In this study, indoor survey data were used to systematically investigate the major factors that affect the heating choices in a city in northern China, where a national pilot program of a cleaner heating policy is being implemented. Our results showed that high-income households were more inclined to adopt cleaner heating methods. Heating costs were the most important and significant factors influencing household heating choices. The higher cost of energy and the devices used for electric and gas heating hindered their adoption. In addition, heating area, education, infrastructure, and policy implementation details were also important factors. At present, the gas substitution policy has functioned better than the electric substitution policy in promoting a transition to cleaner heating. Our results support several policy suggestions to improve the adoption of cleaner heating methods.
6
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Econ. 32, 129–136. https://doi.org/10.1016/j.eneco.2009.06.004. Tonooka, Y., Liu, J., Kondou, Y., Ning, Y., Fukasawa, O., 2006. A survey on energy consumption in rural households in the fringes of Xian city. Energy Build. 38, 1335–1342. https://doi.org/10.1016/j.enbuild.2006.04.011. Vaage, K., 2000. Heating technology and energy use: a discrete/continuous choice approach to Norwegian household energy demand. Energy Econ. 22, 649–666. https:// doi.org/10.1016/s0140-9883(00)00053-0. Xu, Y., Shen, H., Yun, X., Gao, F., Chen, Y., Li, B., Liu, J., Ma, J., Wang, X., Liu, X., Tian, C., Xing, B., Tao, S., 2018. Health effects of banning beehive coke ovens and implementation of the ban in China. Proc. Natl. Acad. Sci. U.S.A. 115, 2693–2698. https://doi.org/10.1073/pnas.1714389115. zhang, J., Koji, K., 2012. The determinants of household energy demand in rural Beijing: can environmentally friendly technologies be effective? Energy Econ. 34, 381–388. https://doi.org/10.1016/j.eneco.2011.12.011. Zhao, N., Zhang, Y., Li, B., Hao, J., Chen, D., Zhou, Y., Dong, R., 2018. Natural gas and electricity: two perspective technologies of substituting coal-burning stoves for rural heating and cooking in Hebei Province of China. Energy Sci. Eng. 7, 120–131. https://doi.org/10.1002/ese3.263. Zhu, X., Yun, X., Meng, W., Xu, H., Du, W., Shen, G., Cheng, H., Ma, J., Tao, S., 2018. Stacked use and transition trends of rural household energy in mainland China. Environ. Sci. Technol. 53, 521–529. https://doi.org/10.1021/acs.est.8b04280.
household energy use in Turkey. Energy Policy 60, 550–557. https://doi.org/10. 1016/j.enpol.2013.05.046. Pachauri, S., Jiang, L., 2008. The household energy transition in India and China. Energy Policy 36, 4022–4035. https://doi.org/10.1016/j.enpol.2008.06.016. Puzzolo, E., Pope, D., Stanistreet, D., Rehfuess, E.A., Bruce, N.G., 2016. Clean fuels for resource-poor settings_ A systematic review of barriers and enablers to adoption and sustained use. Environ. Res. 146, 218–234. https://doi.org/10.1016/j.envres.2016. 01.002. Qin, Y., Tong, F., Yang, G., Mauzerall, D.L., 2018. Challenges of using natural gas as a carbon mitigation option in China. Energy Policy 117, 457–462. https://doi.org/10. 1016/j.enpol.2018.03.004. Rahut, D.B., Behera, B., Ali, A., 2016a. Household energy choice and consumption intensity: empirical evidence from Bhutan. Renew. Sustain. Energy Rev. 53, 993–1009. https://doi.org/10.1016/j.rser.2015.09.019. Rahut, D.B., Behera, B., Ali, A., 2016b. Patterns and determinants of household use of fuels for cooking: empirical evidence from sub-Saharan Africa. Energy 117, 93–104. https://doi.org/10.1016/j.energy.2016.10.055. Richmond, A.K., Kaufmann, R.K., 2006. Is there a turning point in the relationship between income and energy use and/or carbon emissions? Ecol. Econ. 56, 176–189. https://doi.org/10.1016/j.ecolecon.2005.01.011. Scarpa, R., Willis, K., 2010. Willingness-to-pay for renewable energy: primary and discretionary choice of British households' for micro-generation technologies. Energy
7
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Research article
Cleaner heating choices in northern rural China: Household factors and the dual substitution policy ⁎
Zhen Wanga,b, Cai Lib, , Can Cuib, Hui Liub, Bofeng Caic,
T
⁎⁎
a
College of Resource and Environment, Huazhong Agricultural University, Wuhan, 430072, China School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China c Center for Climate Change and Environmental Policy, Chinese Academy for Environmental Planning, Beijing, 100012, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Cleaner heating Dual substitution policy Environmental governance
Household heating is a major contributor to frequent winter haze in northern China. Transition to cleaner household heating is associated with multiple benefits including improved environmental conditions and health of local residents. This study presents an analysis of data from an indoor survey covering 1030 households in 136 villages of Hebi City in the winter of 2018. The main purpose of this study was to reveal the key factors that affect cleaner heating choices under the national pilot program of the dual substitution policy, which targets the replacement of coal heating with gas and electric heating. The survey showed that electric heating is the most popular heating method, and the adoption of cleaner heating rises with income, and energy and device costs may be the major barriers to adopting cleaner heating. Further, multinomial logit regression was used to investigate the household factors and found that income, heating area, energy cost, and education had significant impacts on heating choices. In addition, the gas substitution policy was more effective in promoting the cleaner heating transition than was the electric substitution policy. Results show that more freedom to choose energies and devices, as well as infrastructure for gas supply and centralized heating, is also vital for the adoption of cleaner heating. Our study provides new insights to improve the details of implementation of the dual substitution policy.
1. Introduction Winter heating has been recognized as a critical contributor to air pollution and cause of severe health effects in northern China (Dao et al., 2014). A study revealed that winter heating increased total suspended particulates in the ambient air by 55% in northern China and lowered life expectancy by 5.5 years (Chen et al., 2013). More recently, another study showed that winter heating increased PM10 concentrations by ~42 μg/m3 in northern China, causing a decrease in life expectancy by 3.1 years (Ebenstein et al., 2017). Due to the severe smog in northern China in recent years, air quality and respiratory health have become major topics of concern in the general public, which in turn have impelled the government to commit to improving air quality. Fundamentally, the unusual air quality in northern China is driven by excessive combustion of heating fuel. Therefore, the main strategy to improve winter air quality in the north is to change the heating method, which is currently based on coal burning (Cai and Jiang, 2008; Duan et al., 2014). Policies have an important impact on how household
⁎
energy is used. For example, implementation of the Coal Law has gradually eliminated the utilization of beehive coke ovens, contributing to the reduction of benzo(a)pyrene emissions in the ambient air and a 60% decrease in non-occupational lung cancer cases in China from 1982 to 2015 (Xu et al., 2018). Therefore, the central government of China launched a new policy in 2017, called the Cleaner Winter Heating Plan for the Northern Region (2017–2021), which will promote the use of electricity or natural gas instead of coal for winter heating and thus is often called the dual substitution policy. The goal of the dual substitution policy is to achieve a cleaner heating rate of 50% in 2019 and 70% in 2021, which will benefit the air quality of northern China (Zhao et al., 2018; Chen and Chen, 2019). Relatively speaking, in urban areas with central heating it is easier to achieve a cleaner transition under the dual effects of policy restrictions and financial subsidies, while decentralized household heating, especially in rural areas, usually involves a mixture of various energy and heating methods and thus has more complex circumstances during the energy transition (Zhu et al., 2018; Qin et al., 2018). Energy consumptions in rural households emit a large
Corresponding author. Corresponding author. E-mail addresses: [email protected] (C. Li), [email protected] (B. Cai).
⁎⁎
https://doi.org/10.1016/j.jenvman.2019.109433 Received 8 March 2019; Received in revised form 4 July 2019; Accepted 18 August 2019 0301-4797/ © 2019 Elsevier Ltd. All rights reserved.
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
as well.
amount of pollutants outdoors and indoors (Chen et al., 2016; Jin et al., 2006; Tonooka et al., 2006). Therefore, to effectively achieve the dual substitution policy and improve winter air quality in the north, it is very important to investigate which factors influence the adoption of household heating methods in rural areas. Although cleaner energy and heating technologies are significantly valued by households (Scarpa and Willis, 2010), other barriers hinder the adoption of such cleaner technologies (Puzzolo et al., 2016). Recent studies have attempted to reveal the factors affecting the adoption of cleaner energies. For example, Cai and Jiang (2008) and Liu et al. (2013) both found that household size and income significantly influence the adoption of biomass heating in rural China, while Liu et al. (2013) also recognized that education is an important factor, and households with more educated members are more likely to adopt cleaner heating to reduce CO2 emissions. Duan et al. (2014) used nationwide survey data to investigate the heating patterns in rural and urban China and found that income per capita was a statistically significant factor affecting the adoption of energy types. Feng et al. (2011) also suggested that higher income related to diverse energy structures. Pachauri and Jiang (2008) argued that income, expenditure, cost, and location would affect the household energy transition. Similar results were also reported by Jiang and O'Neill (2004). Zhu et al. (2018) found that only liquefied petroleum gas adoption was associated with increased income, whereas income had no association with electricity adoption in rural China. Chen et al. (2006) evaluated energy choices in forest-poor and forest-rich regions in China and found that location and education affected the choice to substitute coal for fuelwood. zhang and Koji (2012) found that income was the key factor in rural energy consumption and rising energy prices have a strong negative impact on the use of energy. In addition to these studies in China, foreign studies have also revealed that factors have important impacts on household energy choices. Specifically, income and expenditures on household energy use were extensively explored (Ekholm et al., 2010; Mensah and Adu, 2015; Vaage, 2000). Mostly, high income and low cost would be more helpful to achieve the household energy transition. Age, family member, physical assets, education, and employment were also found has different impacts on household energy choice and consumption in Bhutan (Rahut et al., 2016a), Nigeria (Baiyegunhi and Hassan, 2014), Turkey (Özcan et al., 2013), India (Khandker et al., 2012) and many others. However, the aforementioned studies mainly discussed the energy structure, total or cooking energy choice, and/or its transition patterns (Alem et al., 2016; Rahut et al., 2016b). Table 1S in supporting information summarized the contents in detail for the existing literature which closely related to this study. There has been relatively less focus directly on heating choice at the household level in northern China or on assessing the effects of policy factors on heating choices. Therefore, a systematic survey and analysis of the factors influencing household adoption of cleaner heating are urgent and essential for advancing the implementation of the dual substitution policy. This study focuses on the rural household heating choice under the dual substitution policy in 2018, which is a timely study investigating the policy and its effects. In this study, an extensive household survey covering 1,030 rural households was conducted in Hebi City, in the Henan province of China, which is a pilot city for the dual substitution policy. Then, statistical analyses were used to determine the factors affecting the household adoption of cleaner heating. Results showed that electric heating is the most popular heating method, and the adoption of cleaner heating rises with income, and energy and device costs may be the major barriers to adopting cleaner heating. Income, heating area, energy cost, and education had significant impacts on heating choices. In addition, the gas substitution policy was more effective in promoting the cleaner heating transition than the electric substitution policy. To our best knowledge, this is the first study aiming to assess the effect of the dual substitution policy on rural household heating choice in northern China. New insights to improve the details of the implementation of the dual substitution policy have been provided
2. Methodology 2.1. Household survey The aim of the survey was to obtain a picture of the current status of cleaner winter heating in rural areas of northern China. The survey was conducted from 25 to 30 November 2018.1,030 households in 136 villages were indoor surveyed, accounting for 3‰ of total households and 18% of total villages in Hebi City, respectively. Our data covered all administrative districts above the township level in Hebi City, including three districts, two counties, and 40 townships. The questionnaire consisted of three sections and 55 questions. The basic information section surveyed the household information, including education level, household size, heating method, cleaner heating transition, the number of family members at home and the days of the members staying at home in the heating season at home, building status, and heating area. The policy information section surveyed the feedback, willingness to adopt, and willingness to pay for cleaner heating. The energy information section surveyed the household energy type, consumption, price, etc. All answers were collected in indoor face-to-face interviews by at least two trained investigators, most of whom were master's candidates from top-ranking universities in China. All answers were hand-filled and then digitized and double checked in Microsoft Excel. Detailed information about the questionnaires please refer to the supporting information. Due to the small numbers of some heating methods, all forms of electric heating (including electric blankets, fans, radiators, air conditioners, etc.) were categorized into a single group, namely electric heating. Central heating, geothermal, and other minor heating methods were categorized as other heating methods. Household factors were collected from the survey and, for simplicity and brevity, abbreviations were used to denote these factors as follows: household size (FM), heating area (AREA), heating energy cost (COST), middle-income household (INC3), high-income household (INC10), electricity substitution policy (ELE), gas substitution policy (GAS), junior school education (middle school), high school education (high school), higher education (Bachelor). Table 2S in supporting information gives the statistics for the above surveyed factors. 2.2. Statistical model In this study, multinomial logit regression was used to model the household heating adoptions. If the household adoption is y = 1, 2, …, J, where J is a positive integer, then there are j mutually exclusive heating choices. Obviously, given the condition x (household factors), J the sum of the probabilities of heating choices is 1: ∑ j = 1 P (yi = j|x i ) = 1. Therefore, multinomial logit regression is a natural extension of binomial logit regression to multi-choice selection. However, multinomial logit regression cannot estimate the coefficients of the influencing factors for all choices (βk, k = 1, 2, …, J) at the same time. The estimation must be based on a benchmark scheme. In comparison with the benchmark scheme, the probability of one household choose heating method j is 1
P (yi = j|x i ) = {
1 + ∑kJ = 2 exp (x′i βk ) exp (x′i βk ) 1 + ∑kJ = 2 exp (x′i βk )
(j = 1) (j = 2, ..., J )
(1)
The result of equation (1) can be obtained by maximum likelihood estimation, and its log likelihood function Li is
ln Li (βi, ..., βJ ) =
J
∑ j=1 1(yi = j) × ln P (yi = j|xi)
(2)
where 1(⋅) is an indicative function, the value of which is 1 if the 2
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
of other heating methods (see Fig. 2a), with a slightly lower frequency with lower costs (< 500 CNY) and a higher frequency with higher cost (> 500 CNY). Electric heating had the highest energy costs. However, the cost curve was still greatest at lower costs, with a peak frequency at 500 CNY, whereas the overall cost distribution was much broader than that of other heating methods, with relatively high frequencies at costs > 750 CNY. One possible reason is that the demand for energy varies greatly among different electric heaters. In general, small electric heaters, such as electric blankets and fans, consume less energy, whereas electric space heaters, such as electric radiators and air conditioners, consume more energy. From the survey, small heaters were the most frequently used electric heater. Fewer households adopted electric space heaters, and the energy cost was around 5 to 10 times (varied with the compared objects) higher than that of households using small heaters. In addition, the costs of auxiliary thermal insulation differed for different heating methods. The thermal insulation cost of other heating methods was still the lowest, followed in turn by coal heating, natural gas heating, and electric heating. However, the cost of energy might be endogenous to the cost of thermal insulation. For example, central heating in northern China is usually paid for according to the heating area. Under sufficient heating, the households have no incentive to invest in thermal insulation. Comparatively, households using small heaters may need to invest a large thermal insulation cost to maintain room temperature to overcome the cold during winter. The total household heating cost (energy + thermal insulation) is shown in Fig. 2b. It can be seen that the costs of electric and natural gas heating were obviously larger than those of the other two categories. In any case, other heating methods were the most economical. However, other heating methods required a large-scale investment in infrastructure with a long-term plan. Therefore, there is no option for ordinary households, especially rural households, to access other heating methods in the short term.
Fig. 1. Heating choices according to income level.
condition in parentheses is true, otherwise 0. By summing up the log likelihood functions of all samples, the coefficient estimations βˆk can be calculated by maximum likelihood estimation. The multinomial logit model assumes the independence of irrelevant alternatives (IIA), implying that removing a certain heating choice does not affect the consistency of the estimation of other choices but only reduces the estimation efficiency. In other words, in the case where the IIA null hypothesis cannot be rejected, there is no systematic difference between the coefficient estimations of the subsample after removing a certain choice and the coefficient estimation of the full sample. The Hausman test is often used to test whether a model violates the IIA assumption.
3.2. Multiple factors influencing household heating choice 3. Results and discussions Multinomial logit regression models were used to further reveal the impacts of household factors on heating choices. The survey results showed that a subset of other heating methods was used simultaneously with electric heating, coal heating, and natural gas heating, and the cost of other heating methods was much lower in each case. Therefore, the dual substitution policy may have a lesser impact on households that use other heating methods. If other heating methods are considered, the model will not converge (the choice is not exclusive, which violates the basic assumptions of logit regressions); thus, we excluded other heating methods in the multinomial logit models. In addition, most other heating methods require long-term infrastructure planning and construction, thus leaving no option for households to use these heating methods in the short term. Therefore, only the available heating choices were considered, including electric heating, coal heating, natural gas heating, and no heating. The sample size used in the multinomial logit models was 832. Table 1 shows the results of multinomial logit regressions, in which coal-fired heating was used as the benchmark. The Hausman test showed that after removing any of the choices, there was no systematic difference in the coefficient estimates of the subsamples. The regression results were authentic. Model 1 in Table 1 shows that household size (FM), energy costs (COST), income levels (INC3 and INC10), and education have significant effects on heating choice, and the signs of coefficients were in line with expectations. The probability of choosing electric heating was positively correlated with the number of family members, household income (the baseline is the low-income group), and education (the baseline is primary school). The survey answers implied that the highincome and high-education groups were more aware of the improvement in air quality associated with electric heating and were less sensitive to costs. The electricity substitution policy showed a positive but
3.1. Influence of income and cost on heating choice The income level determines the household's spending power and has a great impact on heating choice. The rates of household heating choices according to income group are shown in Fig. 1. In general, electric heating was the most popular heating method. The adoption rates were 48.8% in the low-income group (annual household income < 30,000 CNY), 65.3% in the middle-income group (30,000–100,000 CNY), and 65.2% in the high-income group (> 100,000 CNY). Coal-fired heating ranked as the second most popular choice, with adoption rates of 26.1%, 15.9%, and 8.0% in the three income groups, respectively. Approximately 3.9% of low-income households did not use any heaters throughout the winter, whereas the rates were only 0.9% and 0% in the middle-income and high-income groups, respectively. As household income increased, the rate of using cleaner heating increased significantly, whereas that of less-clean heating decreased (Fig. 1). For example, the rate of coal heating decreased significantly with income, whereas that of natural gas heating increased most with income, from 1.8% in the low-income group to 11.6% in the high-income group. One possible reason is that with increasing income, households have greater affordability and are willing to bear the increase in heating costs (the cost of cleaner heating is usually higher than that of coal heating) to obtain a better indoor environment (Richmond and Kaufmann, 2006). Heating costs are important determinants of household heating choice. Kernel curves for household heating energy costs are shown in Fig. 2. A cost curve closer to the y-axis in Fig. 2, with a higher kurtosis, indicates a lower cost. In general, other heating methods had the lowest energy costs. The energy costs of coal heating were very close to those 3
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Fig. 2. Kernel curves of household heating costs (kernel bandwidth 220).
method. Heating area is one of the main concerns in adopting gas heating. The sign of COST in Model 1 is not in line with expectations, probably because the gas subsidies reduced the heating costs. To verify this hypothesis, the interaction between GAS and COST were added in Model 2. The results showed that subsidies (proxied by GAS × COST) had a significant positive effect on gas heating choice. In addition, the sign of COST also changed as expected, indicating that the gas substitution policy has popularized gas heating mainly by reducing the cost of heating operation. The households that only use gas heating were selected to draw the kernel curves of the heating cost in Fig. 3. Subsidies reduced the unit-price of energy usage, they also prompt more energy use in the household which is known as the rebound effect (Liu et al., 2016). The combined effect is shown in Fig. 3a that implementation of the gas substitution policy has shifted the energy cost curve toward lower costs. That is, the blue line lies closer to the y-axis in Fig. 3a. The two line of the total cost (including energy use and
insignificant impact on electric heating choice, possibly because the policy variable was lack of variation. From the field survey, it is found that most households have adopted various forms of electric heating regardless of whether the region had implemented the electricity substitution policy. Surprisingly, the gas substitution policy had a significant positive effect on electric heating choice. A possible reason is that the substitution policy has strictly curbed coal heating and propelled the transition to cleaner heating. However, in most rural areas, pipeline gas is not available due to the lack of infrastructure. It is difficult for such households to reach a natural gas source. Therefore, the cleaner heating must shift to electric heating alternatives, which are much easier to procure than are other heating methods. For gas heating choice, the household members (FM), heating area (AREA), energy costs (COST), and gas substitution policy (GAS) had significant effects. Unlike most forms of electric heating, such as small heaters not affected by the heating area, gas heating is a space heating
Table 1 Factors affecting household heating choices. Model 1
FM AREA COST Income INC3 INC10 Policy ELE GAS
Model 2
Electricity
Gas
No heat
Electricity
Gas
No heat
0.122*** (3.54) 0.00771 (1.33) −0.00732*** (-4.30)
0.316*** (3.42) 0.0356*** (2.65) 0.00849* (1.84)
0.187 (1.29) 0.0690*** (2.79) −0.0411** (-2.34)
0.100*** (2.84) 0.00646 (1.10) −0.0189*** (-4.63)
0.284*** (3.00) 0.0336** (2.46) −0.00766 (-0.95)
0.212 (1.41) 0.0720*** (2.84) 0.0254 (0.24)
0.484*** (2.57) 0.856** (2.14)
−0.182 (-0.33) 0.645 (0.85)
−0.214 (-0.24) −12.03 (-0.01)
0.504*** (2.65) 0.930** (2.29)
−0.0765 (-0.14) 0.747 (0.97)
−0.293 (-0.33) −12.54 (-0.01)
0.245 (1.04) 1.088*** (4.91)
1.686 (1.55) 4.294*** (5.47)
13.73 (0.02) 0.0228 (0.02)
0.269 (1.13) 0.180 (0.54) 0.0946*** (3.19)
1.896* (2.12) 2.966*** (3.00) 0.0115*** (2.63)
14.06 (0.01) 1.444 (0.63) −0.0669 (-0.63)
2.222*** (2.92) 1.189*** (4.23) 0.786*** (4.11) −3.240*** (-4.22) 832 −516.732 0.1853 YES
1.955 (1.33) 1.131 (1.51) 1.018 (1.50) −17.26*** (-5.48)
−11.87 (-0.01) −12.84 (-0.02) 0.174 (0.20) −18.96 (-0.01)
2.192*** (2.88) 1.122*** (3.96) 0.784*** (4.08) −2.149** (-2.56) 832 −508.736 0.1979 YES
2.038 (1.37) 1.176 (1.55) 1.086 (1.57) −15.89*** (-4.92)
−12.50 (-0.01) −13.39 (-0.01) 0.0924 (0.11) −20.74 (-0.01)
GAS*COST Education Bachelor High school Middle school _cons N Log likelihood Pseudo R2 Hausman test
The values in parentheses are t-statistics. *p < 0.1; **p < 0.05; ***p < 0.01. 4
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Fig. 3. Impact of the gas substitution policy on household heating costs (kernel bandwidth 100).
residents' favorite devices and brands were not on the supply list, they hesitated to change heating methods. The energy option was also an important concern, as residents have their own preferences. From this, it can be seen that giving the residents more freedom to choose, rather than using a one-size-fits-all approach, might be more conductive to the implementation of the dual substitution policy. Lacking infrastructure, especially in rural areas, such as no pipeline gas or central heating, also obstructed implementation of the dual substitution policy.
thermal insulation; Fig. 3b) shows similar results. Namely, a lower kurtosis at lower costs and longer tails at higher costs (~15000 CNY) is shown after the implementation of gas substitution policy. But the two lines become much closer. It implies that, in the current policy environment, the high cost of purchasing heating devices and installation offset the effects of subsidies. Especially, device cost is usually high onetime investments, which may be an important factor hindering the adoption of gas heating. Compared with electric heating, the influence of income and education was not significant, indicating that cost was the decisive factor for adopting gas heating. Some of the low-income and middle-income households chose no heating in the winter. The models showed that heating area and heating costs were the major factors affecting the decisions of these households. The higher the heating costs and larger the heating area, these households were more likely to give up winter heating.
3.4. Challenges, policy implications and limitations The above results show that the key challenges in achieving the goal of cleaner air are the lack of energy infrastructure and policy flexibility. The severe smog in winter in northern China, energy structure reform is imperative. In large developed cities, central heating is the major method of winter heating. Through coal-to-gas technology remodeling, the cleaner heating renovation of most households in urban areas can be achieved in a short time. The scale effect of central heating brings another advantage in that better pollution-control technologies can be used in the heating systems to further reduce emissions. However, in small- and medium-sized cities, central heating infrastructure is lacking, and household-based heating and non-point source emissions occupy a dominant position. In our survey, households that can access central heating accounted for only 2.81% of the total survey. Therefore, at the current stage, establishing green and sustainable infrastructures, including clean energy supplies and centralized heating supplies, should be a priority in small- and medium-sized cities in northern China. Meanwhile, it is essential to strengthen the new urbanization strategy and encourage scattered rural residents to cluster in small- and medium-sized cities. Policy flexibility is also an important factor in achieving clean air goals. According to the survey, local residents hope to have more energy and device options. However. the current policy gives households less autonomy, resulting in the mismatch of the needs of residents and the supply of policies. Giving them the right to choose their own energy and devices helps the implementation of the policies. Therefore, for areas that cannot be provided with central heating, the dual substitution policy should be propagandized and implemented in a more flexible manner. Our results revealed the above challenges and proposed the following policy suggestions to address them: (1) the government should formulate more flexible rules to allow households to freely choose clean heating methods, devices, and brands and use postsubsidies to reduce households' heating and device costs, which are the major obstacles for low-income households adopting cleaner heating methods; (2) dispel the doubts held by households through better and proactive propaganda, making residents aware of the environmental and health benefits of cleaner heating, thus elevating the residents’ acceptance of cleaner heating; (3) revitalize the rural economy in various ways to increase the household income of rural residents, which will significantly reduce their sensitivity to heating costs and increase their willingness to adopt cleaner heating methods. Although our results revealed several important factors affecting a
3.3. Other policy implementation factors We surveyed which policy improvements promote a household's willingness to adopt cleaner heating methods by providing a −5 to 5 scoring system for answers, in which −5, 0, and 5 represented extremely disagree, neutral, and extremely agree with the proposed options in the questionnaire, respectively. The scores and error bars of major factors affecting the willingness to adopt cleaner heating are shown in Fig. 4. Operation subsidies and device subsidies ranked as the two top appeals, with average scores > 1, implying that reducing device and operation costs is still the most important factor in successfully achieving the dual substitution policy goals. The survey results were consistent with the results of the above empirical models. Currently, new devices are uniformly provided by government-approved suppliers. Suspicion of the device stability and the number of optional devices were other major concerns hindering adoption. When local
Fig. 4. Other factors affecting heating choice. 5
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Acknowledgement
household's heating choices, the conclusions still need further exploration. First, due to the short time span since the implementation of the dual substitution policy, there is a lack of continuous observation and survey data. While it is anticipated that factors such as heating area and cost will remain the most important factors in long-term observations, the roles of other household factors require more data to arrive at a robust conclusion. Secondly, although the total number of surveyed households was sufficient to characterize the heating choices of Hebi City, the data used to calculate the cost curve of a single heating method are limited, which may bias the cost comparisons between different heating methods. In summary, changes in household heating patterns in northern China under the dual substitution policy require more observation and research to systematically assess their impacts. Thirdly, our results show that the effect of the gas substitution policy functions better than the electric substitution policy, possibly because the cost of electric heating is higher than that of gas heating, and the existing forms of electric heating include a variety of methods from various small heaters to space heaters, which makes it more difficult for the government to implement heating subsidies to effectively reduce household heating costs. However, it is currently very difficult to collect and distinguish household-level subsidies for electric heating. The above hypothesis must be verified in future studies. Compared with previous studies, this study provides some new findings while the other main conclusions are consistent with previews studies. The major difference from the previous research results is mainly reflected in the rural energy structure. For example, Duan et al. (2014) found biomass was the dominant energy fuel for heating in 2012 in China and Zhu et al. (2018) found an increase of coal in heating during 1992–2012. In contrast, our study found electricity is the major energy form in Hebi city. The difference may be attributed to the different survey time and the rapid transition caused by the dual substitution policy. The transition was evidenced by the heating energy structure change in Beijing (Barrington-Leigh et al., 2019), which shows that policies have effectively changed the energy structure into electricity dominated one. Regarding the factors affecting the heating choices, our results show consistency with the research of Mensah and Adu (2015) in Ghana that income and education have a positive effect on cleaner energy adoption. Similar results were also found by Vaage (2000) in India that income, heating area, and family size were positive factors and by Scarpa and Willis (2010) that cost was a negative factor. These consistent results confirmed that household factors determined the effectiveness of the dual substitution policies. Therefore, policy improvement considering the aforementioned household concerns is essential.
This work is funded by the project entitled An Emission-TransportExposure Model Based Study on the Evaluation of the Environmental Impact of Carbon Market (No. 71673107) supported by the National Natural Science Foundation of China. The work is also financially supported by the National Key Research and Development Program of China (No.2018YFC0214001). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jenvman.2019.109433. References Alem, Y., Beyene, A.D., Köhlin, G., Mekonnen, A., 2016. Modeling household cooking fuel choice: a panel multinomial logit approach. Energy Econ. 59, 129–137. https://doi. org/10.1016/j.eneco.2016.06.025. Baiyegunhi, L.J.S., Hassan, M.B., 2014. Rural household fuel energy transition: evidence from Giwa LGA Kaduna state, Nigeria. Energy Sustain. Dev. 20, 30–35. https://doi. org/10.1016/j.esd.2014.02.003. Barrington-Leigh, C., Baumgartner, J., Carter, E., Robinson, B.E., Tao, S., Zhang, Y., 2019. An evaluation of air quality, home heating and well-being under Beijing's programme to eliminate household coal use. Nature Energy 4, 416–423. https://doi.org/10. 1038/s41560-019-0386-2. Cai, J., Jiang, Z., 2008. Changing of energy consumption patterns from rural households to urban households in China: an example from Shaanxi Province, China. Renew. Sustain. Energy Rev. 12, 1667–1680. https://doi.org/10.1016/j.rser.2007.03.002. Chen, H., Chen, W., 2019. Potential impact of shifting coal to gas and electricity for building sectors in 28 major northern cities of China. Appl. Energy 236, 1049–1061. Chen, L., Heerink, N., van den Berg, M., 2006. Energy consumption in rural China: a household model for three villages in Jiangxi Province. Ecol. Econ. 58, 407–420. https://doi.org/10.1016/j.ecolecon.2005.07.018. Chen, Y., Ebenstein, A., Greenstone, M., Li, H., 2013. Evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River policy. Proc. Natl. Acad. Sci. U.S.A. 110, 12936–12941. https://doi.org/10.1073/pnas. 1300018110. Chen, Y., Shen, G., Liu, W., Du, W., Su, S., Duan, Y., Lin, N., Zhuo, S., Wang, X., Xing, B., Tao, S., 2016. Field measurement and estimate of gaseous and particle pollutant emissions from cooking and space heating processes in rural households, northern China. Atmos. Environ. 125, 265–271. https://doi.org/10.1016/j.atmosenv.2015.11. 032. Dao, X., Wang, Z., Lv, Y., Teng, E., Zhang, L., Wang, C., 2014. Chemical characteristics of water-soluble ions in particulate matter in three metropolitan areas in the north China plain. PLoS One 9, e113831. https://doi.org/10.1371/journal.pone.0113831. Duan, X., Jiang, Y., Wang, B., Zhao, X., Shen, G., Cao, S., Huang, N., Qian, Y., Chen, Y., Wang, L., 2014. Household fuel use for cooking and heating in China: results from the first Chinese environmental exposure-related human activity patterns survey (CEERHAPS). Appl. Energy 136, 692–703. https://doi.org/10.1016/j.apenergy.2014. 09.066. Ebenstein, A., Fan, M., Greenstone, M., He, G., Zhou, M., 2017. New evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River Policy. Proc. Natl. Acad. Sci. 114, 10384–10389. https://doi.org/10.1073/ pnas.1616784114. Ekholm, T., Krey, V., Pachauri, S., Riahi, K., 2010. Determinants of household energy consumption in India. Energy Policy 38, 5696–5707. https://doi.org/10.1016/j. enpol.2010.05.017. Feng, Z.-H., Zou, L.-L., Wei, Y.-M., 2011. The impact of household consumption on energy use and CO2 emissions in China. Energy 36, 656–670. https://doi.org/10.1016/j. energy.2010.09.049. Jiang, L., O'Neill, B.C., 2004. The energy transition in rural China. Int. J. Glob. Energy Issues 21, 2–26. https://doi.org/10.1504/IJGEI.2004.004691. Jin, Y., Ma, X., Chen, X., Cheng, Y., Baris, E., Ezzati, M., 2006. Exposure to indoor air pollution from household energy use in rural China: the interactions of technology, behavior, and knowledge in health risk management. Soc. Sci. Med. 62, 3161–3176. https://doi.org/10.1016/j.socscimed.2005.11.029. Khandker, S.R., Barnes, D.F., Samad, H.A., 2012. Are the energy poor also income poor? Evidence from India. Energy Policy 47, 1–12. https://doi.org/10.1016/j.enpol.2012. 02.028. Liu, J., Sun, X., Lu, Bin, Zhang, Y., Sun, R., 2016. The life cycle rebound effect of airconditioner consumption in China. Appl. Energy 184, 1026–1032. https://doi.org/ 10.1016/j.apenergy.2015.11.100. Liu, W., Spaargaren, G., Heerink, N., Mol, A.P.J., Wang, C., 2013. Energy consumption practices of rural households in north China Basic characteristics and potential for low carbon development. Energy Policy 55, 128–138. https://doi.org/10.1016/j. enpol.2012.11.031. Mensah, J.T., Adu, G., 2015. An empirical analysis of household energy choice in Ghana. Renew. Sustain. Energy Rev. 51, 1402–1411. https://doi.org/10.1016/j.rser.2015. 07.050. Özcan, K.M., Gülay, E., Üçdoğruk, Ş., 2013. Economic and demographic determinants of
4. Conclusion Winter heating is one of the main causes of haze in northern China. To reduce air pollution and its impacts on health, it is necessary to encourage local residents to adopt cleaner heating methods. In this study, indoor survey data were used to systematically investigate the major factors that affect the heating choices in a city in northern China, where a national pilot program of a cleaner heating policy is being implemented. Our results showed that high-income households were more inclined to adopt cleaner heating methods. Heating costs were the most important and significant factors influencing household heating choices. The higher cost of energy and the devices used for electric and gas heating hindered their adoption. In addition, heating area, education, infrastructure, and policy implementation details were also important factors. At present, the gas substitution policy has functioned better than the electric substitution policy in promoting a transition to cleaner heating. Our results support several policy suggestions to improve the adoption of cleaner heating methods.
6
Journal of Environmental Management 249 (2019) 109433
Z. Wang, et al.
Econ. 32, 129–136. https://doi.org/10.1016/j.eneco.2009.06.004. Tonooka, Y., Liu, J., Kondou, Y., Ning, Y., Fukasawa, O., 2006. A survey on energy consumption in rural households in the fringes of Xian city. Energy Build. 38, 1335–1342. https://doi.org/10.1016/j.enbuild.2006.04.011. Vaage, K., 2000. Heating technology and energy use: a discrete/continuous choice approach to Norwegian household energy demand. Energy Econ. 22, 649–666. https:// doi.org/10.1016/s0140-9883(00)00053-0. Xu, Y., Shen, H., Yun, X., Gao, F., Chen, Y., Li, B., Liu, J., Ma, J., Wang, X., Liu, X., Tian, C., Xing, B., Tao, S., 2018. Health effects of banning beehive coke ovens and implementation of the ban in China. Proc. Natl. Acad. Sci. U.S.A. 115, 2693–2698. https://doi.org/10.1073/pnas.1714389115. zhang, J., Koji, K., 2012. The determinants of household energy demand in rural Beijing: can environmentally friendly technologies be effective? Energy Econ. 34, 381–388. https://doi.org/10.1016/j.eneco.2011.12.011. Zhao, N., Zhang, Y., Li, B., Hao, J., Chen, D., Zhou, Y., Dong, R., 2018. Natural gas and electricity: two perspective technologies of substituting coal-burning stoves for rural heating and cooking in Hebei Province of China. Energy Sci. Eng. 7, 120–131. https://doi.org/10.1002/ese3.263. Zhu, X., Yun, X., Meng, W., Xu, H., Du, W., Shen, G., Cheng, H., Ma, J., Tao, S., 2018. Stacked use and transition trends of rural household energy in mainland China. Environ. Sci. Technol. 53, 521–529. https://doi.org/10.1021/acs.est.8b04280.
household energy use in Turkey. Energy Policy 60, 550–557. https://doi.org/10. 1016/j.enpol.2013.05.046. Pachauri, S., Jiang, L., 2008. The household energy transition in India and China. Energy Policy 36, 4022–4035. https://doi.org/10.1016/j.enpol.2008.06.016. Puzzolo, E., Pope, D., Stanistreet, D., Rehfuess, E.A., Bruce, N.G., 2016. Clean fuels for resource-poor settings_ A systematic review of barriers and enablers to adoption and sustained use. Environ. Res. 146, 218–234. https://doi.org/10.1016/j.envres.2016. 01.002. Qin, Y., Tong, F., Yang, G., Mauzerall, D.L., 2018. Challenges of using natural gas as a carbon mitigation option in China. Energy Policy 117, 457–462. https://doi.org/10. 1016/j.enpol.2018.03.004. Rahut, D.B., Behera, B., Ali, A., 2016a. Household energy choice and consumption intensity: empirical evidence from Bhutan. Renew. Sustain. Energy Rev. 53, 993–1009. https://doi.org/10.1016/j.rser.2015.09.019. Rahut, D.B., Behera, B., Ali, A., 2016b. Patterns and determinants of household use of fuels for cooking: empirical evidence from sub-Saharan Africa. Energy 117, 93–104. https://doi.org/10.1016/j.energy.2016.10.055. Richmond, A.K., Kaufmann, R.K., 2006. Is there a turning point in the relationship between income and energy use and/or carbon emissions? Ecol. Econ. 56, 176–189. https://doi.org/10.1016/j.ecolecon.2005.01.011. Scarpa, R., Willis, K., 2010. Willingness-to-pay for renewable energy: primary and discretionary choice of British households' for micro-generation technologies. Energy
7