Impacts of residential energy consumption on the health burden of household air pollution: Evidence from 135 countries

Energy Policy 128 (2019) 284–295

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Impacts of residential energy consumption on the health burden of household air pollution: Evidence from 135 countries

T

Qiang Wanga,b, Mei-Po Kwanc,d, Kan Zhoue, Jie Fane, , Yafei Wange, Dongsheng Zhanf ⁎

a

State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, Fujian Normal University, Fuzhou 35007, PR China b School of Geographical Sciences, Fujian Normal University, Fuzhou 35007, PR China c Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA d Department of Human Geography and Spatial Planning, Utrecht University, the Netherlands e Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100191, PR China f College of Economics and Management, Zhejiang University of Technology, Hangzhou 310023, PR China

ARTICLE INFO

ABSTRACT

Keywords: Burden from household air pollution Residential energy consumption Energy transition Spatial regression models

Knowledge about the links between burden from household air pollution (B-HAP) and residential energy consumption (REC) is essential for optimizing residential energy supply mix and improving the quality of indoor air worldwide. However, the literature on this topic from a perspective of energy transition is still lacking. This study investigates the relationship between the variation in the B-HAP and the structural transition of REC using cross-sectional data of 135 countries during 1990–2015. The results indicate that countries with high B-HAP are clustered in Africa and Asia, which are mainly middle- and low-income countries. Meanwhile, with the structural transition of REC, the global B-HAP has exhibited a decreasing trend. Moreover, the findings show that residential electricity use has a greater impact on B-HAP reduction than other household fuels. Although the impacts of liquefied petroleum gas usage changed considerably during the study period, its contribution to reducing the B-HAP remains highly significant, while household natural gas use exhibited a significant and stable effect on B-HAP reduction. In contrast, solid biomass use showed an increasingly adverse impact on the BHAP, and the impact of coal use on the B-HAP became statistically significant since 2010, with an increasing trend.

1. Introduction Associations between long-term exposure to air pollution and increased mortality have been extensively studied in scientific research (Lelieveld et al., 2015; Ji and Zhao, 2015; Evans et al., 1984; Kolokotsa and Santamouris, 2015; Liu et al., 2018; Chen et al., 2017; Lott et al., 2017; Rao et al., 2017; Yu et al., 2018). The World Health Organization (WHO) estimated that 7.3 million people worldwide die annually due to air pollution, and approximately 60% of these deaths are attributable to household exposure to smoke from dirty cookstoves and fuels (WHO, 2018). In a sense, household air pollution (HAP), which is generated by household fuel combustion in and around the home, has become the world's leading environmental health risk. Since residential energy consumption (REC) is the major source of HAP, it has considerable impacts on mortality and morbidity in important ways. Along with increasing population and major improvement in life quality, global

⁎

REC increased by over 33.7% during 1990–2015. Meanwhile, as the dramatic increase in households with access to electricity, natural gas, liquefied petroleum gas (LPG), and other fuels with less or no pollution, the death rate from HAP dramatically decreased from 63.68 to 38.72 per 100,000 people during the period (IHME, 2015). Despite such improvement in household air quality, approximately 3.1 billion people worldwide still rely on polluting energy sources for cooking, heating, and lighting (WHO, 2018), and the global health risk from REC persists for a long time, especially for developing countries. However, any policy or strategy that aims at reducing the health risk attributable to HAP requires a better understanding of the impacts of REC on the burden from HAP (B-HAP). Biomass (wood, crop residues, and animal dung) and solid fossil fuels (coal and coke) burned inside poorly ventilated spaces with thermally inefficient stoves has been suggested as the primary cause of chronic obstructive pulmonary disease, high blood pressure and lung cancer in adults, pneumonia in

Corresponding author. E-mail address: [email protected] (J. Fan).

https://doi.org/10.1016/j.enpol.2018.12.037 Received 28 August 2018; Received in revised form 19 December 2018; Accepted 21 December 2018 0301-4215/ © 2019 Elsevier Ltd. All rights reserved.

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Table 1 Definition of the variables. Variables

Definition

Rate_D Per_B Per_C Per_P Per_N Per_E

Sum of the years of life lost from premature death and the years lived with a disability caused by a disease attributable to household air pollution per 100,000 persons Primary solid biomass use per capita converted to kilograms of oil equivalent (kgoe) divided by the national population. Coal use per capita converted to kilograms of oil equivalent (kgoe) divided by the national population. Liquefied petroleum gas and kerosene use per capita converted to kilograms of oil equivalent (kgoe) divided by the national population. Natural gas use per capita converted to kilograms of oil equivalent (kgoe) divided by the national population. Electricity use per capita converted to kilograms of oil equivalent (kgoe) divided by the national population.

children, and even cataracts and low birth weight (Padmavati and Pathak, 1959; Boy et al., 2002; Smith et al., 2004; Dherani et al., 2008; Bautista et al., 2009; International Agency for Research on Cancer, 2010; Kurmi et al., 2010; Xiao et al., 2015; Mortimer et al., 2017; Matawle et al., 2017). In addition, to reduce the health risks of exposure to HAP, the impacts of interventions on HAP were assessed in some early studies (Albalak et al., 2001; Smith, 2002; Bruce et al., 2004, 2015; Chengappa et al., 2007; Dutta et al., 2007; Armendáriz-Arnez et al., 2010; Clark et al., 2013; Quansah et al., 2017; Perera, 2017). In most instances, improved cleaner-burning stove designs have been proven to substantially reduce air pollution emissions and exposures compared with the baseline, but air pollutant concentrations are still higher than the WHO guideline in countries with high B-HAP (Clark et al., 2013). Furthermore, evidence on the health risks from HAP suggests that controlling exposure to HAP could reduce the risk of multiple adverse health outcomes for children and adults by 20–50% (Bruce et al., 2015). In order to achieve these benefits, intervention strategies must shift towards accelerating people's access to clean fuels. Meanwhile, as there are no specific norms for HAP in developing countries, implementing strategies to create public awareness and improvement in ventilation and modification in the pattern of fuel usage should be urgent (Rohra and Taneja, 2016). Although existing studies from multiple disciplines suggested a vital role of solid fuels in exposure assessment and proposed a series of actions on HAP and exposure reduction, there is limited knowledge about the relationship between the B-HAP and REC from the macro perspective of environment and energy management, which is essential for optimizing residential energy supply mix and improving the quality of household air worldwide. In light of the aforementioned considerations, this study uses cross-sectional data and models to address the following questions: (1) How have the global patterns of the B-HAP changed over the last 25 years? (2) Which fuels increased or decreased the likelihood of the B-HAP at the national level? (3) What are the main policy implications of the findings from this study? Analysis in this study proceeds as follows. The paper first describes changes in the distribution patterns of the global B-HAP during 1990–2015 by quantitively measuring the extent to which the burden of death and disease rates vary geographically, that is critical for obtaining a better understanding of the global patterns. The study also focuses on quantifying the impact of REC on the B-HAP, and identifying the specific impacts of different household fuels on the B-HAP and their variations over the study period. The results of this study can guide the formulation of national energy policies for improving household air quality, optimizing energy supply mix, and enhancing the well-being of residents.

this study is that they can help us identify whether there is any evidence linking the spatial distribution pattern of the B-HAP to cross-sectional dependence, which is known as spatial autocorrelation or spatial dependence in the spatial analysis literature (Pesaran, 2004). The notion of spatial autocorrelation or spatial dependence means that observations made at different locations may not be independent, and those made at nearer locations tend to be more similar than those located farther away. This phenomenon has attracted considerable attention over the past decade (Chudik et al., 2011; Holly et al., 2010; Pesaran et al., 2013), and it is increasingly recognized that neglecting such dependency can lead to biased estimates and spurious inferences (Chudik et al., 2011). To mitigate statistical biases and inferential errors, spatial analysis tools like the Moran's I index can be used to evaluate whether the spatial distribution pattern of the B-HAP is clustered, dispersed, or random and perform in-depth exploration of contemporaneous dependence across space in this pattern. 2.1. Empirical model To analyze the impact of REC on the B-HAP, a cross-sectional regression model was built, and six parameters were selected and defined in detail as shown in Table 1. Among them, Rate_D was defined as the dependent variable representing the national B-HAP that has been proposed to measure by different metrics in existing literature, including the absolute number of deaths, the age-standardized death rate, and the disability-adjusted life years (DALY) lost (WHO, 2005; IEA, 2018; WHO, 2016; IHME, 2018; Cohen et al., 2017). The main drawback of measuring the absolute number of deaths is that it fails to take into account population size and the age of the individuals included in these statistics. Thus, the higher number of deaths in a country might be attributed to its larger or older population. Meanwhile, according to this metric, a child who dies from an illness is counted exactly the same as an older individual who died a few months earlier than expected. In contrast, the age-standardized death rate refers to the number of deaths per 100,000 people, which is standardized based on the age structure of the population. Considering the purpose of this study, the B-HAP not only includes the potential years of life lost due to premature death but also the equivalent years of 'healthy' life lost from living in poor health or disability caused by HAP. However, this measure fails to take into account the years lost due to disability. Compared with the aforementioned measures, DALY has an overwhelming advantage of combining mortality and morbidity into a single and common metric. Therefore, in this study, the DALY rate (DALYs per 100,000 people) estimated by the Institute for Health Metrics and Evaluation (IHME, 2016) was selected as a metric of the B-HAP. Moreover, as using different fuels has different impacts on the household environment according to the International Energy Agency's report (IEA, 2018), this study divides the main fuels used in the residential sector into three categories based on their physical properties: solid fuels, liquid and gaseous fuels, and electricity and heat. Generally, solid fuels include various solid materials that can be burnt to release energy, including coal and solid biomass. Liquid and gaseous fuels in the residential sector mainly consist of liquefied petroleum gas (LPG) and natural gas, which are normally stored in pressurized containers. Solid fuels have been used throughout human history and are still used worldwide because they are cheaper, easier to

2. Methods and data Given the main purposes of this study, a cross-sectional regression model is built, and three spatial analysis techniques including the Moran's I index, a spatial lag model (SLM) and a spatial error model (SEM), are applied to examine the changes in the spatial distribution pattern of the B-HAP across the world and the relationships between the B-HAP and REC at the country level. Particularly, one of the main reasons for the application of the three spatial analysis techniques in 285

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extract, and more readily available. However, solid fuels are typically used indoors or in partly enclosed cooking areas, burnt in poorly ventilated and inefficient stoves, and have higher carbon, nitrate, and sulfate emissions (EPA, 2015; IPCC, 2002, 2006), thus leading to higher level of air pollution and human exposure to smoke emitting from incomplete combustion (WHO, 2005). In this study, five types of residential fuels are observed as extraneous factors, including coal, solid biomass, LPG, natural gas, and electricity, which may potentially influence the B-HAP. As shown in Table 1, the independent variables include Per_B, Per_C, Per_P, Per_N, and Per_E, which refer to per-capita household use of solid biomass, coal, LPG, natural gas, and electricity, respectively. In addition, all variables were Ln-transformed, and a cross-sectional regression model describing the relationship between the B-HAP and REC parameters was formulated as follows:

LN(Rate _Dt ) =

t

+

+

1t LN

4t LN

(Per _Bit ) +

(Per _Pit ) +

2t LN

5t LN

(Per _Cit ) +

(Per _Eit ) +

3t LN

it

(Per _Nit ) (1)

where Rate_Dt is the sum of the years of life lost from premature death and the years lived with disability caused by a disease attributable to HAP per 100,000 persons in year t; and t refers to the study year, due to the lack of long-term data on the health effects of HAP, this study applies cross-sectional data of six years (1990, 1995, 2000, 2005, 2010, and 2015); i = 1,…, n represents the number that represents a particular sampled country, is a constant, 1t , 2t , 3t , 4t ,and 5t are longterm elasticity estimates of REC parameters, it is an error term.

Fig. 1. Distribution of z-scores and p-values associated with the different patterns.

statistical significance to determine whether or not to reject the null hypothesis (see Fig. 1). When the z-score indicates statistical significance, a Moran's I value near + 1.0 indicates spatial clustering while a value near −1.0 indicates spatial dispersion. In addition, a positive value for Ii indicates that feature i has neighboring features with similarly high or low attribute values and it is part of a cluster. A negative value for Ii indicates that it has neighboring features with dissimilar values and it is therefore an outlier. Then the cluster/outlier type can be distinguished between statistically significant clusters of high values (HH), clusters of low values (LL), outliers in which values are surrounded primarily by low values (HL), and outliers in which low values are surrounded primarily by high values (LH).

2.2. Spatial analysis of the spatial distribution of the B-HAP 2.2.1. Moran's / index To explore the spatial autocorrelation or cross-sectional dependence of the B-HAP among the sampled countries and the changes in the global distribution and evolvement of the B-HAP, the Moran's I index was adopted based on national DALY data in 1990, 1995, 2000, 2005, 2010, and 2015. This method was developed by Moran (Moran, 1950), which not only can provide a set of quantitative indicators of the extent to which the B-HAP vary geographically, but also help us examine and identify those spatial clusters and dispersion in the global distribution patterns. To date, Moran's I has been commonly used to examine spatial patterns (e.g., clustered, dispersed, or random) (Li et al., 2007; Helbich et al., 2012). Specifically, analysis of spatial autocorrelation involves evaluating the existence and degree of overall clustering with the global Moran's I index (IG) and identifying local hotspots and outliers with the Local Indicator of Spatial Association (LISA) (Ii). Generally, IG only summarizes the characteristics of spatial patterns over the whole study area, while Ii represents the individual Moran's I for unit i. In this sense, IG is a summation of individual cross-product Ii. In this study, both IG and Ii are used to investigate the global and local clustering characteristics of the spatial patterns of the B-HAP, and these indexes can be defined as:

N

IG =

Ii =

si2 =

N i

xi

wij

x si2

N i

×

wij (xi N i

(x i

x )(xj

2.2.2. Spatial regression analysis Ordinary least squares (OLS) is a type of linear least squares method for estimating the unknown parameters in a linear regression model, which is widely used in statistics. Fundamentally, this method works on the assumptions that the random error of the regression equation should have a mean of zero, a constant variance, and a normal distribution. However, these assumptions may not always be satisfied when a value observed in one location depends on the values observed at neighboring locations because of the existence of spatial dependence that makes the variables and error terms of spatial data deviate from the normal distribution. Therefore, if there is spatial autocorrelation in the spatial patterns of a social or economic characteristic, using OLS is likely to result in biased, even erroneous estimates and statistical inferences. In this regard, considering the possibility of spatial autocorrelation across countries, a spatial lag model (SLM) and a spatial error model (SEM) were used in this study to address spatial autocorrelation and explore the impact of spatial non-stationarity on research results. Then, by comparing the results from the SLM, SEM, and OLS, the relatively reliable method and measurements were proposed. Specifically, the SLM can control the level of spatial autocorrelation by adopting a lag variable for the spatial effect to estimate a regression model, and its equation can be defined as follows (Anselin and Griffith, 1988):

x)

x )2

(2)

N

×

wij (x i j = 1, j i

N j = 1, j i

(x j

N 1

x )(xj

x)

(3)

x )2 (4)

where N is the number of countries indexed by i and j; x i is the DALY rate in country i; x is the mean of x i , si2 refers to sample variance; wij is a matrix of spatial weights. To conduct a statistical test, the null hypothesis states that there is no pattern (i.e., the expected pattern is a hypothetical random distribution). Meanwhile, a z-score and p-value are used to assess

Y = WY + X +

(5)

where Y is a N × 1vector of observation on the dependent variable; WY 286

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is N × 1vector of spatial lags for the dependent variable Y; is the coefficient of the spatial lags, which indicates the effect of the dependent variable in the neighbors on the dependent variable in the focal area; is a K × 1vector of regression coefficients for each X that is a N × K matrix of observations on the exogenous explanatory variables; and is a N × 1vector of the normally distributed random error term. Unlike the SLM, the SEM evaluates the extent to which the clustering of variable Y can be accounted for with reference to the clustering of its error term W . Thus, the SEM takes the form described as follows:

Table 2 Moran's I value during 1990–2015. Moran's I

z-score

p-value

Indentification

1990 1995 2000 2005 2010 2015

0.557 0.553 0.545 0.536 0.519 0.468

20.703 20.570 20.076 19.919 19.283 17.463

0.000 0.000 0.000 0.000 0.000 0.000

Clustered Clustered Clustered Clustered Clustered Clustered

3. Results and discussion

Y= X+ = W +µ

(6)

3.1. Changes in the spatial patterns of the B-HAP

where W is a spatial lag for the errors; is the autoregressive coefficient, and µ is another error term. Therefore, Eq. (1) can be transformed into an SLM (7) and SEM (8) as follows:

LN(Rate_Dt ) =

Year

t WY

+

+

t

+

1t LN

(Per_Bit ) +

3t LN (Per_Nit ) +

2t LN

As shown in Table 2, high Moran's I values indicate that there were significantly clustered patterns worldwide during 1990–2015. On the whole, since 1990, high B-HAP countries were mainly concentrated in Africa and Asia (Fig. 2), where most MICs and LICs are located. Based on the distribution of Ii, two HH clusters (a cluster in Sub-Saharan Africa and a China-India cluster) and two LL clusters (a cluster in Europe and the other one in the Middle East) worldwide were identified in Fig. 3, and this distribution pattern seemed stable during the study period. Specifically, in 1990, there were 30 countries in Africa with a DALY rate exceeding 5000, and 10 countries with a DALY rate between 3000 and 5000, accounting for 69.8% and 47.6% of the selected countries respectively. Meanwhile, four of the top five countries with high B-HAP — Niger (14,701), Guinea (10,660), Sierra Leone (10,387), and Burkina Faso (9555) — are located in Africa. Although most countries have exhibited a general decline over the last few decades, the risk of mortality and morbidity in Africa from HAP was still markedly higher than that of other regions (Fig. 2). For example, although the number of African countries with a DALY rate exceeding 2000 decreased from 44 to 26 by 2015, as shown in Tables 3, 4, Africa still accounted for 72.2% of the total number of countries with a DALY rate exceeding 2000 worldwide, even higher than its share in 1990 (54.4%). Asia exhibits the second highest B-HAP among nine regions. There were 16 Asian countries with a DALY rate exceeding 2000 in 1990. However, with rapid economic development and a significant improvement in household conditions, the number of countries in Asia with high DALY rate exceeding 2000 dropped to 5 by 2015. In addition, the results showed that IG dropped down from 0.557 to 0.468 between 1990 and 2015, which indicates that the extent of spatial clustering gradually decreased over time, as the global DALY rate dramatically decreased from 2574 to 908 DALYs between 1990 and 2015. In particular, the number of countries with a DALY rate exceeding 5000 dramatically decreased from 43 in 1990–1 by 2015. Especially, that number in Africa declined from 30 to 1 during the study period.

(Per_Cit )

4t LN (Per _Pit ) +

5t LN

(Per _Eit ) +

t

(7)

LN(Rate _Dt ) = t W + +

3t LN

t

+

1t LN

(Per _Bit ) +

(Per _Nit ) +

4t LN

2t LN

(Per _Cit )

(Per _Pit ) +

5t LN

(Per _Eit ) (8)

+u

2.3. Data In this study, the household fuel-use data was obtained from the International Energy Agency (IEA, 2018). Data on the DALY rate (DALYs per 100,000 persons) of 188 countries and regions were estimated by the Institute for Health Metrics and Evaluation (IHME, 2016). The DALY rate can be described as follows.

DALY rate =

YLL + YLD POP

YLL = NUMDeath × EXPDeath YLD = NUMDisease × WEIDisease × DURDisease

(9)

where YLL refers to the years of life lost due to premature mortality attributable to indoor air pollution, which basically corresponds to the number of deaths (NUMDeath ) multiplied by the standard life expectancy at the age at which death occurs (EXPDeath) ; while YLD denotes the years lost due to disability for people living with the unhealth condition or the consequence caused by indoor air pollution, which is estimated by the number of incident cases (NUMDisease ) in a particular period multiplied by the average duration of the disease (DURDisease ) and a weight factor (WEIDisease ) that reflects the severity of the disease on a scale from 0 (perfect health) to 1 (dead); POP represents the population scale in a country. Given the main purpose of this study, the estimation method is only briefly described here, but more detail can be found in the studies of Lim et al. (2012) and Smith et al. (2014). Indeed, it was a large international effort to estimate the envelope of death, disease, and injury associated with HAP across the world (IHME, 2012), thus there is few globally available alternative metrics to substitute for it. In this regard, although the metric of the DALY rate was limited to the risk assessment of using solid fuels for cooking, it still can provide a reasonable gauge at the national scale on how the REC affects human health. In addition, given that specific purpose of fuel use for cooking, heating, lighting, and so on is difficult to measure separately, this study just adopted the consumption of each fuel in the whole residential sector.

3.2. Links between the distribution pattern of the B-HAP and REC Due to the limitation of access to household fuel-use data, 135 countries can be observed. The OLS, SLM and SEM regression results listed in Table 5 show that all regression models explain most of the variation (adjusted coefficients of determination, Radj2, was over 0.608) in the dependent variable (Rate_D). In addition, the Akaike information criterion (AIC), the White test (Wh) for heteroskedasticity of the OLS model, the multicollinearity condition number (MCN) evaluation for the multicollinearity among the five independent variables (including Per_B, Per_C, Per_P, Per_N, and Per_E), and Moran’ I calculation for the residuals of three (Moran’ I_R) were also listed in Table 5. The results of the three regression models for the six study years indicate that: (1) there is no multicollinearity among the five independent variables because the MCN parameter was lower than 20; (2) AIC values for the three models are nearly the same, which implies that 287

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Fig. 2. Spatial patterns of B-HAP from 1990 to 2015.

all models have similar quality; (3) except for 1990 and 1995, high probabilities of the White tests (Wh) suggest the nonexistence of heteroskedasticity in the OLS models, indicating that the variances of the errors in the OLS models are constant; and (4) in OLS models, Moran's I_R are highly significant, indicating strong spatial autocorrelation of the residuals, while those in SLM and SEM models are insignificant, suggesting that there is no significant spatial autocorrelation and systematic error in the residuals of the SLM or SEM regression model. Comparing the three models, the two spatial regression models have higher R2adj values and lower AIC values, which means that these two models have better explanatory power than the OLS model for Rate_D. Furthermore, except for 1990, the SEM, with a lower AIC and higher R2adj, works better than the SLM. Overall, the results show that independent variables Per_E, Per_N, and Per_P are significantly and negatively associated (p < 0.01) with Rate_D, which indicates that the increased use of electricity, LPG, and natural gas in the residential sector likely plays an important role in reducing the DALY rate. In contrast, Per_B and Per_C are significantly and positively associated with Rate_D, indicating that burning more solid biomass and coal will significantly increase the national risk of premature mortality and morbidity associated with HAP. Fig. 4 shows the changes in regression coefficients of all independent variables, and the results reveal that the roles of different fuels in the residential sector changed over time. As expected, the use of solid biomass (Per_B) has an increasing contribution to national B-HAP, with its regression coefficient increased from 0.127 to 0.344 between 1990 and 2015. Surprisingly, the impact of using electricity (Per_E) on the B-HAP is greater than that of solid biomass, and the Per_E coefficient changed remarkably from −0.404 to −0.960 between 1990 and 2015, which implies that more residential electricity use in a country is associated with a smaller risk of mortality and morbidity due to HAP.

Meanwhile, the absolute value of the Per_P coefficient decreased continuously from 0.345 to 0.264 between 1990 and 2015, indicating that as household LPG usage/need declined, its influence on reducing the BHAP seemingly decreased during the study period; while the effect of natural gas (Per_N) use on the B-HAP seems relatively stable. In contrast, an increase in coal usage shows a significantly adverse influence on human health in recent years, particularly in Asian MICs like China and India, where coal use has become a potential threat to human health since 2010. 3.3. Discussion To further discuss the impacts of the residential energy transition on the change on the B-HAP, the Energy Ladder Theory was adopted in this study (Hosier and Dowd, 1987; Heltberg, 2004, 2005; Hiemstra-van der Horst and Hovorka, 2008; Schlag and Zuzarte, 2008; Kroon et al., 2013), which states that regional socio-economic development plays a vital role in modern energy demand growth and shifts households from primitive (solid biomass) and transitional fuels (coal, kerosene) towards modern fuels (electricity, LPG, natural gas, renewable and clean fuels). From this perspective, in this study, 135 countries are assigned to three income groups according to the method used in the World Bank Atlas (World Bank, 2017): low-income countries (LICs), middle-income countries (MICs), and high-income countries (HICs). As one of the advanced fuels, average per-capita electricity use in the residential sector increased from 155.66, 24.18, and 2.30 kg of oil equivalent (kgoe) to 208.89, 48.10, and 4.25 kgoe (Fig. 5) during 1990–2015 in HICs, MICs, and LICs, respectively, and its share in REC correspondingly grew from 34.5%, 15.9%, and 1.1–43.2%, 25.0%, and 5.3% (Fig. 6). It seems that electricity use is more common in HICs, which has been the most convenient way to transfer energy to a 288

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Fig. 3. LISA maps during 1990–2015.

household appliance. This is consistent with the Energy Ladder Theory. According to evidence derived from earlier experimental studies (Khandker et al., 2009; Dinkelman, 2011; Barnes, 2014; Aklin et al., 2016; Barrona and Torerob, 2017), the extensive use of electricity can surely improve the quality of household air by reducing the use of candles, kerosene lamps, and even solid biomass and coal to satisfy people's cooking, heating, and lighting needs, that may be one of the most important reasons why the burdens of death and disease are much lighter in HICs than those in MICs and LICs. Similar to electricity, both residential LPG and natural gas usage also varied significantly between countries due to its relatively high costs. In 1990, the per-capita residential LPG and natural gas usage in HICs were 123.53 and 91.15 kgoe, which accounted for 24.0% and13.4% of residential energy usage (Fig. 6). However, the use of these two household fuels in MICs and LICs dramatically lagged far behind HICs, as shown in Figs. 7 and 8. In addition, per-capita residential LPG consumption in both HICs and MICs shows a decreasing trend, and the gap between low-, middle- and high-income countries

narrowed significantly, which likely led to the decrease in its impact on the global distribution pattern of the B-HAP. In contrast, per-capita natural gas use increased significantly during the study period in MICs and HICs, but its impact on the global distribution pattern of the B-HAP seemed stable. Compared to the aforementioned advanced fuels, solid biomass and coal have been used widely in MICs and LICs (see Figs. 9 and 10) owing to their cheaper price, ease of extraction, and availability (Hosier and Kipondya, 1993; Bhagavan and Giriappa, 1995; Brouwer and Falcao, 2004; Mirza and Kemp, 2009). Despite quick access to modern fuels in LICs and MICs during 1990–2015, and the shares of solid biomass decreased respectively from 96.2% and 50.4–90.7% and 41.7% (Fig. 6), respectively. In this regard, the predominance of solid biomass use for cooking and heating has not changed considerably over the the last 25 years. In addition, the impact of coal use became statistically significant since 2010 and with an increasing trend, especially in Asian MICs like China and India, has become the second residential fuel that is potentially threatening to human health.

Table 3 Geographic distribution of countries by intervals of DALY rate (DALYs per 10,000) in 1990. Regions

Over 5000

3000–5000

2000–3000

1000–2000

500–1000

Below 500

Central America Asia Eurasia Europe Middle East Africa North America Oceania South America Total

1 8 1 0 1 30 0 1 1 43

1 4 2 0 0 11 0 3 0 21

0 4 4 2 0 3 0 1 1 15

5 2 1 4 2 3 0 4 1 22

5 0 2 2 2 2 1 1 6 21

9 5 5 23 11 3 3 3 4 66

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Table 4 Geographic distribution of countries by intervals of DALY rate (DALYs per 10,000) in 2015. Regions

Over 5000

3000–5000

2000–3000

1000–2000

500–1000

Below 500

Central America Asia Eurasia Europe Middle East Africa North America Oceania South America Total

0 0 0 0 0 1 0 0 0 1

0 3 0 0 0 9 0 3 0 15

1 2 0 0 0 16 0 1 0 20

0 6 2 1 0 14 0 2 0 25

2 5 3 3 1 4 0 3 1 22

18 7 10 27 15 8 4 4 12 105

In brief, as shown in Fig. 11, holding medical care and living conditions constant, the transition from traditional solid fuels to modern clean fuels in the residential sector decreased the global B-HAP significantly, especially in LICs, where the average of B-HAP fell more dramatically than in the other two groups, but still bore the highest burden among three groups.

reliability. Although our findings show that promotion of electricity used for household's life has a positive impact on reducing the B-HAP, it is not expected that electricity will replace other fuels for cooking and heating in African and Asian countries with high B-HAP as the primary fuel. This inference can be drawn from the current pattern of REC in member countries of the Organization for Economic Co-operation and Development (OECD), most of which are developed economies and have advanced energy systems. In 2015, although electricity approximately accounts for 37% of total REC in these countries, 60% of which is used for lighting and running other appliances rather than for cooking and heating; whereas the essential fuels used for household cooking and heating are still natural gas and LPG that accounts for more than 80% of the total fuels used for these two purposes. One of the possible reasons is the higher financial (and environmental) cost of electricity use when compared with natural gas and LPG, given the air pollution from thermal electricity generation and power losses during the process of transformation and distribution. In this regard, policymakers in those countries with high B-HAP should launch initiatives to encourage urban households to use electricity, LPG, natural gas for some specific purposes (e.g., using electricity for lighting and running appliances, using LPG or natural gas for water heating and cooking), which could stabilize or even reduce solid fuel consumption. For rural dwellers, owing to low per-capita income and the absence of economies of scale in LICs and some MICs, it is difficult to establish a commercially viable electricity, LPG and natural gas distribution network. But frequent delivery service of refillable compressed gas cylinders can be promoted as the first step to switching from traditional solid fuels to modern gaseous fuels, which has been widely used in the rural areas of China according to our survey. Therefore, local governments can subsidize the up-front costs of buying gas stoves and cylinders to potential households, and provide market support and credit facilities to gas suppliers. With abundant renewable energy resources and the rapid expansion of modern science and technology, renewable clean energy has a promising potential to greatly improve the quality of indoor air and household life in these Asian and African countries with high B-HAP. Particularly, in the face of population growth and dramatic economic growth, China has implemented various supportive policies to develop centralized renewable power generating systems. However, with the rapid construction of renewable energy plants in China, the increasing amounts of variable power has posed challenges to existing grid's carrying capacity and distribution. From this respect, power grid optimizing and planning, targeted at accommodating more widespread use of renewable energy, should be put into action by the Chinese government. However, this practice will not be economically feasible for the other LICs and MICs with a large rural population and fluctuating economy due to the initial massive investment costs in new power plants and grids construction and low household income. In that case, distributed renewable power generating systems, especially photovoltaic panel and biogas plant, have been shown to be practically sensible for reducing solid fuels use in rural or off-grid households

4. Policy implications The potential for reducing the B-HAP, to a large extent, depends on how residential fuels switch under a more socially and environmentally conscious policy framework. Given the current trends of residential energy supply-consumption and socio-economic development levels in African and Asian countries with high B-HAP, this study put forward several constructive suggestions for fuel switching targeted at the household air pollution mitigation. First, it is likely that solid fuels will remain an important source of energy for many decades in these countries with high B-HAP, especially for cooking. Thus policies that advocate the adoption of clean and efficient cookstoves are of vital importance to minimize the adverse health impacts of solid fuels use, since inefficient and unsustainable cooking and heating practices have been universally acknowledged as the primary threat to human health (Smith et al., 2004; International Agency for Research on Cancer, 2010; Baumgartner et al., 2011; Mortimer et al., 2017; Matawle et al., 2017; WHO, 2018). Moreover, the evidence from this study indicates that the use of solid biomass remains prevalent in the developing world, especially in African LICs, where the total residential consumption of solid biomass is expected to take up an over 70% share of residential energy needs for cooking and heating. Meanwhile, using coal for household's life in Asian MICs is still common, especially in rural areas (Wang, 2014; IEA, 2006). In this context, a practically reasonable approach is to improve the efficiency of solid fuel use through the provision of clean cookstoves and enhanced ventilation while cooking or heating water. As reported by REN21 (2005), improved cookstoves can save from 10% to 50% of biomass or coal consumption for the same cooking service provided and can reduce indoor air pollution by up to one half. In fact, efforts towards this direction have been ongoing since the 1980s (Agbemabiese et al., 2012; Bhattacharyya, 2012), but the rates of adoption of clean cookstoves have been slow in LICs (Bhattacharyya, 2014; UNDP-WHO, 2009) because of their high costs and short service life (Weir, 2018). Therefore, the government in these countries should develop sound plans and take actions to promote the adoption of clearn cookstoves, such as advertising to raise people's awareness of the health and environmental benefits of clean cookstoves, and offering financial incentives and regularly organizing repairing/replacing services to encourage households to use clean cookstoves, especially for rural residents. In addition, the manner in which modern fuels can be complementary to conventional energy should be regarded as essential for optimizing against the two key constraints of affordability and 290

7.313*** 0.127*** 0.009 − 0.060 − 0.404*** − 0.345*** 0.663 456.893 – − 0.002

8.610*** 0.152*** 0.009 − 0.071 − 0.502*** − 0.385*** 0.608 469.912 37.404** 0..046** 8.306

291

− 0.750***

***

− 0.505

0.697 484.479 19.445 0.054*** 9.589

Per_E

Per_P

R2adj AIC Wh Moran's I_R MCN

*** Indicates significance at 1% level. ** Indicates significance at 5% level. * Indicates significance at 10% level. √ Indicates the adopted model.

8.886*** 0.233*** 0.111 − 0.096**

C Per_B Per_C Per_N

0.727 477.838 – − 0.002

− 0.458

7.711*** 0.206*** 0.082 − 0.087 − 0.645*** ***

SLM

Year 2005

SLM√

OLS

Year 1990

OLS

Variables

C Per_B Per_C Per_N Per_E Per_P R2adj AIC Wh Moran's I_R MCN

Variables

Table 5 Evaluation of the stepwise regression models.

0.737 475.563 – 0.023

**

− 0.398

**

**

*-

− 0.781*-

8.688*** 0.223*** 0.095 − 0.163*-

SEM

√

8.556*** 0.124*** 0.016 − 0.101 − 0.481*** − 0.371*** 0.647 463.913 – 0.013

SEM

0.719 484.155 22.981 0.061*** 11.249

− 0.397

***

− 0.882**

8.445*** 0.317*** 0.188** − 0.115**

OLS

Year 2010

8.918*** 0.174*** 0.098 − 0.134 − 0.602*** − 0.484*** 0.692 460.847 29.100** 0.042** 8.793

OLS

Year 1995

0.744 478.940 – − 0.004

**

− 0.369-

***

− 0.768-

**

7.404*** 0.286*** 0.157* − 0.110-

SLM

7.652*** 0.151*** 0.078 − 0.121** − 0.506*** − 0.434*** 0.733 456.893 – − 0.001

SLM

0.753 475.590 – 0.042

− 0.293

***

− 0.924***

8.300*** 0.314*** 0.179** − 0.150***

SEM

√

8.730*** 0.177*** 0.055 − 0.158 − 0.617*** − 0.428*** 0.736 454.141 – 0.021

SEM√

0.712 496.773 29.294 0.075*** 11.448

− 0.318

***

− 0.927***

7.815*** 0.377*** 0.289*** − 0.125**

OLS

Year 2015

9.012*** 0.192*** 0.112 − 0.131** − 0.657*** − 0.532*** 0.703 471.789 25.912 0.047*** 9.533

OLS

Year 2000

0.727 492.241 – − 0.003

**

− 0.295-

***

− 0.813-

**

6.854*** 0.336*** 0.245* − 0.118-

SLM

7.865*** 0.172*** 0.090 − 0.114*** − 0.574*** − 0.479*** 0.734 464.434 – − 0.001

SLM

***

0.748 484.145 – − 0.045

− 0.264-

***

− 0.960-

**

7.877*** 0.344*** 0.258*** − 0.143-

SEM√

8.872*** 0.186*** 0.076 − 0.163*** − 0.702*** − 0.446*** 0.740 463.551 – 0.016

SEM√

Q. Wang et al.

Energy Policy 128 (2019) 284–295

Energy Policy 128 (2019) 284–295

Q. Wang et al.

Fig. 4. Changes in regression coefficients of different household fuels. Fig. 7. Changes in LPG use per capita of HICs, MICs and LICs during 1990–2015.

Fig. 5. Changes in electricity use per capita of HICs, MICs and LICs during 1990–2015.

(Weir, 2018; Michalena and Hills, 2018). In this regard, supportive policies and financial incentives should be offered to households to encourage them to use renewable and clean energy from distributed renewable power generating systems. Furthermore, locally-based power purchasing entities should be granted purchasing authorizations from the governments in order to collect surplus renewable energy supplies from local households and resell them to a wider area.

Fig. 8. Changes in natural gas use per capita of HICs, MICs and LICs from 1990–2015.

residential energy consumption (REC) on the burden attributable to household air pollution (B-HAP) from a perspective of energy transition. The results revealed that countries with high B-HAP are clustered in Africa and Asia, which mainly comprise middle-income and low-income countries (MICs and LICs). However, with the structural transition of REC, the global B-HAP has exhibited a decreasing trend. Moreover, the findings revealed the changes in the patterns of residential fuel usage. In particular, residential electricity use has a greater impact on

5. Conclusion The primary purpose of this study is to investigate the impact of

Fig. 6. Changes in household energy consumption mixture of HICs, MICs and LICs from 1990 to 2015. 292

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Q. Wang et al.

There are several limitations in this study. The first one is the lack of long-term B-HAP data. The existing raw data on the B-HAP were estimated on the basis of solid fuels usage for household cooking, not by combustion of nonsolid fuels for space heating and lighting. Despite this limitation, the data could represent the major impacts of REC on human health risks. In addition, the lack of long-term data on the B-HAP also limits our ability to apply the panel model to carry out this study. Within this constraint, cross-sectional data for six years (1990, 1995, 2000, 2005, 2010, and 2015) were collected and used. Second, as the main purpose of this study is to focus on the impacts of REC on the BHAP, some social and economic factors were not considered and included in the study. Third, given that specific use of electricity for cooking, heating, lighting, and so on is difficult to measure separately, this study adopted the total electricity use in the residential sector. This may affect the regression results because electricity is mainly used for lighting, cooling, rather than cooking.

Fig. 9. Changes in solid biomass use per capita of HICs, MICs and LICs during 1990–2015.

CRediT authorship contribution statement Qiang Wang: Conceptualization, Writing - original draft. MeiPo Kwan: Writing - review & editing. Kan Zhou: Data curation. Jie Fan: Conceptualization, Formal analysis. Yafei Wang: Data curation. Dongsheng Zhan: Visualization. Acknowledgments The authors would like to thank the anonymous reviewers for their valuable comments on the manuscript. This study is funded by the National Natural Science Foundation of China (Gran Number: 41671126). and the Science and Technology Department of Fujian Province (Gran Numbers: 2016R10325 and 2018R0030).

Fig. 10. Changes in coal use per capita of HICs, MICs and LICs during 1990–2015.

Author contributions Q.W. and K.Z. conceived, designed and implemented the experiments; Q.W. analyzed the results and wrote the paper with K.Z. and Y.W.; M.-P.K. contributed to refining and revising the paper. Conflicts of interest The authors declare no conflict of interest. Fig. 11. Changes in B-HAP of different groups.

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