Household Energy Use Research Articles

Adressing Energy Demand and Climate Change through the Second Law of Thermodynamics and LCA towards a Rational Use of Energy in Brazilian Households.

This study focuses on a typical Brazilian household through the lens of sustainable development, regarding energy demand and GHG emissions. The analysis encompasses both the direct and indirect energy, exergy consumption, and GHG emissions (quantified by life cycle assessment) associated with the usual routine of a household. The household is modeled as a thermodynamic system to evaluate inputs (food, electricity, fuels for transportation) and outputs (solid and liquid residues). The hypothesis is that each input and output contains CO2,eq emissions and exergy derived from its physical-chemical characteristics or production chains. Each household appliance is modeled and tested as a function of external parameters. The contribution of several industries was obtained to the total GHG emissions and exergy flows entering and exiting the household (e.g., fuels for transportation, food, gas, electricity, wastewater treatment, solid waste). It was verified that urban transportation was the flow with the highest GHG and exergy intensity, ranging between 1.49 and 7.53 kgCO2,eq/day and achieving 94.7 MJ/day, almost five times higher than the calculated exergy demand due to electricity. The second largest flow in GHG emissions was food due to the characteristics of the production chains, ranging from 1.6 to 4.75 kgCO2,eq/day, depending on the adopted diet. On the other hand, the electricity presented low GHG emissions due to the main energy sources used to generate electricity, only 0.52 kgCO2,eq/day. Moreover, the chemical exergy of the solid waste was 9.7 MJ/day, and is not irrelevant compared to the other flows, representing an interesting improvement opportunity as it is entirely wasted in the baseline scenario.

Multi-regional input–output analysis of the relationship between environmental footprints and the import dependence of Japanese prefectures

This study examined the proposition that a subnational region with a low dependence on an external supply of goods and services has low environmental impacts based on consumption-based accounting. Thus, we investigated the relationship between the level of economic dependence of Japanese prefectures on domestic and international imports of goods and services and the environmental footprints (EFs) of their households. EFs were calculated for greenhouse gas (GHG) emissions and use of land, water, and materials using datasets for the year 2015. We estimated the EFs through an environmentally extended input–output analysis using multi-regional input–output datasets, EXIOBASE3. We conducted linear regression analyses of the prefectural household EFs with their socioeconomic, demographic, and geographical factors, including import ratios representing the import dependence of prefectures. The results indicated that the prefectures with a low import ratio, all other factors being equal, had low indirect GHG, land, water, and material footprints. In addition, we found that food and food-related consumptions were dominant contributors to these footprints. When the GHG emissions from household fuel combustion, electricity use and household waste incineration were included in the GHG footprint, the emissions from household energy use were the largest contributor to the footprint. The results indicate that substituting locally sourced foodstuffs and renewable energy for internationally imported food products and non-renewable energy/fuels could reduce multiple environmental impacts of households in the prefectures.

The Impact of the COVID-19 Pandemic on the Decision to Use Solar Energy and Install Photovoltaic Panels in Households in the Years 2019–2021 within the Area of a Selected Polish Municipality

The aim of this article is to show the impact of the COVID-19 pandemic on electricity consumption and, consequently, on decisions regarding the installation of photovoltaic panels using the example of a selected local authority in Poland—the Szemud Municipality. The analysis was conducted in 2022 and covered the years 2019–2021. An attempt was made to explore the factors that may have triggered an increase in the use of solar energy in households and identify the determinants of installing photovoltaic panels in the period under analysis. Previous analyses of the PV market (and the impact of the pandemic on it) have so far focused on the market as a whole, either in macro or global terms, while studies on smaller municipalities have been limited to examining changes in electricity consumption levels during the COVID-19 pandemic and during lockdown. Therefore, a research gap was identified in that there are no studies analyzing the reasons for the shift from conventional to PV-assisted energy in households, with the COVID-19 pandemic as the background of these changes. The literature research showed that there are currently no studies attempting to establish a link between the increased interest in this type of energy by local authorities and the COVID-19 pandemic. The research confirmed the hypothesis of increased interest in household PV during the pandemic. The main conclusions of the study boil down to the need for further support as well as promotion of the use of solar energy. In addition, the results derived from the empirical research indicate the need to take action at a policy level to counter adverse trends regarding undesirable social behavior.

A health risk matrix for assessing the impacts of heat and cold exposure in residential buildings

• BACKGROUND AND AIM With people in developed countries spending 70% of their time at home, the impact of the built environment on human health is vital. Although there has been ample epidemiological evidence on the relationship between high/low outdoor temperature exposures and adverse health impacts, there is a paucity of research investigating how residential characteristics and human behaviours might affect temperature-related health risks. This research aims to fill this gap. • METHODS A health risk matrix was obtained by applying a four-stage spatiotemporal health risk assessment framework (comprising hazard identification, exposure assessment, exposure-response assessment and risk characterization) to estimate the temperature-cardiovascular mortality relationship in Greater Taipei, Taiwan, using paired observations of outdoor-indoor temperatures and PM2.5 concentrations in dwellings. • RESULTS Residential characteristics and household energy use acted as moderators, and indoor temperature functioned as a mediator in the temperature-mortality relationship. A three-dimensional matrix projected outdoor temperature, human vulnerability and dwelling type along three axes to visualise the cardiovascular mortality risk. Human vulnerability represents the interaction between outdoor temperature and household energy use. By multiplying the risk levels (1-3) of each of the three axes, a total of 27 levels of health risk are obtained. • CONCLUSIONS This study encourages carefully-planned climate change mitigations and adaptations to achieve healthier indoor environments: (1) Strategies for reducing temperature-related cardiovascular mortality should incorporate the energy use for heating/cooling, which may affect human vulnerability and be affected by the household income, prevalence of air conditioning systems, green area, and population rate of older people. (2) Building retrofits for improving energy efficiency should carefully consider dwelling thermal types, which may vary with occupants’ behaviours and cause unintended health consequences when indoor temperature closely follows outdoor temperature and makes dwellings overheat or excessively cold. • KEYWORDS Health risk assessment, Health matrix, Temperature-related mortality, Dwellings, Human behaviours

Health impacts of solid fuel use in Ghana: A nationally representative assessment of cooking-related burns

BACKGROUND AND AIM Burns and burn-related injuries are still a major public health problem. Every day, over 30,000 people suffer new severe burns worldwide. Low- and middle-income countries (LMICs) bear a disproportionate burden (70%) of these burns. While burn prevention strategies have successfully been implemented in high-income countries, a paucity of data regarding the incidence and risk factors of burns in LMICs limits their expansion. Here, we aimed to describe the country-level incidence of serious cooking-related burns in Ghana and to identify household-level risk factors for adults and children. METHODS We conducted a nationally representative household energy use survey in Ghana in 2020. The final sample included 7,389 households across 370 enumeration areas in all 16 regions of Ghana. An adapted version of the Global Alliance for Clean Cookstoves Burns Surveillance Module was administered to each respondent. We computed incidence rates and 95% confidence intervals using the Byar’s approximation and conducted univariate logistic regression to identify potential risk factors. RESULTS Out of 7,389 households, we documented 129 serious cooking-related burns which had occurred in the previous 12 months. Slightly more than half (55%) of those burns occurred among adults. The incidence rate (95% CI) of cooking-related burns per 1000 person-years was 9.7 (7.7; 12) and 11 (8.5; 14) among adults and children respectively. Among adults, the odds (95% CI) of experiencing a serious cooking-related burn were 2.29 (1.02; 5.14) and 2.40 (1.04; 5.55) times higher among primary wood and charcoal users respectively compared to primary LPG users. No children burns were documented in households where LPG was primarily used. CONCLUSIONS Using a nationally representative sample, we characterized the incidence of cooking-related burns across demographic groups in Ghana. Solid fuel use compared to LPG use doubled the odds of experiencing such burns. KEYWORDS Burns; Solid fuel; Energy access; LMICs; Ghana

Energy labels and heuristic decision-making: The role of cognition and energy literacy

Energy labels are used worldwide to overcome the inefficiency in household energy use. Presenting energy efficiency in grade-like categories has been found to induce heuristic decision-making, known as the “class valuation effect”. This study investigates whether visualising energy efficiency using a continuous-scale label increases consumers’ consciousness when purchasing appliances. We examine this question in an online survey with randomised decision tasks conducted in China and the Netherlands. In the Dutch sample, the effectiveness of continuous-scale labels depends on individuals’ holistic cognitive tendency and the type of comparison they are faced with, whereas the Chinese sample is not sensitive to these moderators. Moreover, energy literacy shows mixed influence on individual decisions. Results suggest that representing energy efficiency on continuous scales can be used as supportive visual information to facilitate purchase decisions. Regarding energy education, knowledge that directly links to daily energy use seems to translate well into consumers’ conscious decisions.

In a New Light: Histories of Women and Energy

In a New Light: Histories of Women and Energy

Field measurements of indoor and community air quality in rural Beijing before, during, and after the COVID-19 lockdown.

The coronavirus (COVID‐19) lockdown in China is thought to have reduced air pollution emissions due to reduced human mobility and economic activities. Few studies have assessed the impacts of COVID‐19 on community and indoor air quality in environments with diverse socioeconomic and household energy use patterns. The main goal of this study was to evaluate whether indoor and community air pollution differed before, during, and after the COVID‐19 lockdown in homes with different energy use patterns. Using calibrated real‐time PM2.5 sensors, we measured indoor and community air quality in 147 homes from 30 villages in Beijing over 4 months including periods before, during, and after the COVID‐19 lockdown. Community pollution was higher during the lockdown (61 ± 47 μg/m3) compared with before (45 ± 35 μg/m3, p < 0.001) and after (47 ± 37 μg/m3, p < 0.001) the lockdown. However, we did not observe significantly increased indoor PM2.5 during the COVID‐19 lockdown. Indoor‐generated PM2.5 in homes using clean energy for heating without smokers was the lowest compared with those using solid fuel with/without smokers, implying air pollutant emissions are reduced in homes using clean energy. Indoor air quality may not have been impacted by the COVID‐19 lockdown in rural settings in China and appeared to be more impacted by the household energy choice and indoor smoking than the COVID‐19 lockdown. As clean energy transitions occurred in rural households in northern China, our work highlights the importance of understanding multiple possible indoor sources to interpret the impacts of interventions, intended or otherwise.

Understanding building energy efficiency with administrative and emerging urban big data by deep learning in Glasgow

With buildings consuming nearly 40% of energy in developed countries, it is important to accurately estimate and understand the building energy efficiency in a city. A better understanding of building energy efficiency is beneficial for reducing overall household energy use and providing guidance for future housing improvement and retrofit. In this research, we propose a deep learning-based multi-source data fusion framework to estimate building energy efficiency. We consider the traditional factors associated with the building energy efficiency from the Energy Performance Certificate (EPC) for 160,000 properties (30,000 buildings) in Glasgow, UK (e.g., property structural attributes and morphological attributes), as well as the Google Street View (GSV) building façade images as a complement. We compare the performance improvements between our data-fusion framework with traditional morphological attributes and image-only models. The results show that including the building façade images from GSV, the overall model accuracy increases from 79.7% to 86.8%. A further investigation and explanation of the deep learning model are conducted to understand the relationships between building features and building energy efficiency by using SHapley Additive exPlanations (SHAP). Our research demonstrates the potential of using multi-source data in building energy efficiency prediction with high accuracy and short inference time. Our paper also helps understand building energy efficiency at the city level to help achieve the net-zero target by 2050.

Renewable Energy Acceptance by Households: Evidence from Lithuania

Although renewable energy adoption in the residential sector has increased significantly in the EU due to the governmental policies, aiming to reduce the barriers of renewable energy penetration, the full potential of renewable energy deployment in households is still not realized due to the behavioral and other barriers. One of the most important factors in the adoption of renewable energy technologies in households is the decision-making to implement renewables; therefore, the behavioral economics insights should be taken into account during the analysis of renewable energy acceptance by households. The paper provides a systematic literature review on renewable energy use in households by analyzing policies and measures, which could increase the use of renewable energy in households by overcoming the major barriers. The dynamics of renewable energy consumption in EU households was performed by applying Eurostat data, and the empirical case study was conducted in Lithuania to understand the main reasons of renewable energy acceptance by the household. Even though the use of renewable energy sources has increased significantly in the EU member states during the recent years, the study has found the following most common barriers that the traditional policies are unable to overcome: (1) high upfront cost and long pay-back period, (2) a lack of information and knowledge, (3) low priority of environmental concern, (4) resistance to change; human habits. The case study shows that the majority of Lithuanian households would like to use renewable energy technologies in their homes, but they encounter financial difficulties and lack of infrastructure. The policy recommendations were developed based on the results of the conducted study.

Inclusive urban building energy modeling through socioeconomic data: A persona-based case study for an underrepresented community

Urban Building Energy Modeling provides a way to simulate energy use at the scale of a neighborhood or city, rather than the typical simulation of a single building. This can be a powerful tool to reduce current energy usage and to guide future planning efforts. This switch in scales is crucial in reducing energy use and planning more sustainable and resilient cities. Critical work is being done to utilize and improve urban building energy modeling. However, current workflows and tools do not account for the socioeconomic factors of study areas. This paper identifies the impacts of socioeconomic factors and establishes a framework that can be used to gather the data required to run accurate urban building energy modeling studies that consider the urban context. A case study utilizing the Urban Modeling Interface Rhino plugin to simulate the energy use of 110 single-family residential structures in the Grove Park neighborhood of Atlanta, Georgia demonstrates the framework. The results of the study analyze current energy use patterns, compare underserved neighborhood-specific archetype definitions to default residential archetype templates, and investigate the neighborhood's performance under future weather scenarios. The study shows that within a single neighborhood the Energy Use Intensity can vary by up to 92 kWh/m2 based on building envelope condition and occupancy patterns. Default archetype inputs can dramatically underestimate or overestimate the energy use of households in a low resource community. Urban energy models must account for demographic and socioeconomic factors to create an accurate model that reflects lived experiences.

Influence of household factors on energy use in Vietnam based on path analysis

Economic growth and corresponding energy demand have been rapidly increasing in developing countries where household determinants have a remarkable influence. This study draws on the impact of household accessible factors on energy use in Vietnam which remains a new case study with rare information on energy data in the residential area. Path analysis is applied to illustrate a complex structure of how household factors with different multi-unit impacts energy use on the same scale. The direct and indirect correlations between household factors and energy use can be clarified by graphical visualization of Tree Plot Path and Correlation Matrix. The results shed light on the “influence level” of direct and indirect factors, where household size, housing floor area, and the number of air conditioning significantly affect total energy consumption in the case study. The use of cooking utensils is significantly proportional to the occupancy of people at home while non-cooking electricity consumption derives from the number of air-conditioners. By adapting to different environmental and social characteristics, this concise and modifiable model syntax shows the levels of household impacts to offer appropriate scenarios for energy-saving policies in areas with hard-to-reach statistical data such as Vietnam and other developing countries in the region.

A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation.

Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.

The effects of attitudes on household energy behavior. A study of climate change concern, responsibility, and awareness in European societies

ABSTRACTObjectiveSeveral attempts in environmental research have been made to investigate which factors are crucial for people to act pro‐environmentally. Yet, research results remain rather inconsistent, especially across countries. The present article aims to explore whether climate change attitudes and household energy use are associated with European citizens, independent of the country.MethodsThis cross‐national study uses items from the recent European Social Survey 2016 “climate change and energy” module measuring people's attitudes to climate change and energy preferences at the European level by using a multilevel analysis.ResultsI found that at the individual level, both climate change concern, responsibility, and awareness are important to explain variance in energy‐saving behavior, although in different ways. The results suggest that using only variables directly related to attitudes is too limited to explain household energy behavior. The present study indicates that gender, income, and education have a significant impact on behavior. At the country level, GDP and unemployment rate seem to predict less in relation to household energy behavior.ConclusionClimate change attitudes seem to have an impact on people's energy‐saving behavior, even after controlling for socioeconomic characteristics. However, it is important to distinguish between different measures of household energy behavior.

Technical analysis on household energy consumption: LMDI decomposition index and innovative

For decades, Indonesia’s Economic Development had achieved great high growth rates by using more labor input, capital investment, physical and human, and fostering competition. Over time, Indonesia’s energy intensity, which historically always been high compared to other industrialized economies. This study aimed to analyze decomposition factors of energy consumption by analyzing the link between the other nine factors and household energy use. The Logarithmic Mean Divisia Index were used to identify the determinant variables that drove a household’s energy usage. This study used a Time-series VECM model for prediction model analysis. The Logarithmic Mean Divisia Index (LMDI) and decomposition index were used to identify the determining technological, economic, and social factors driving a household’s energy consumption evolution. We analyzed the outcomes after applying the LMDI. We provided econometric individual time series VEC models, which we used for three targeting models: social, economic, and technical factors. We discovered that economic variables were the most significant factors that aggravated the change in household energy consumption based on LMDI. Technical factors, on the other hand, were the lowest. We found that the government might reduce its energy use over time by improving technological factors. Energy efficiency had been a cornerstone in managing growing energy use. The declining relevance of fewer labor-force in the previous decade was reflected in the negative indication of social factors at home. We discovered that the models were more stable while using Time Series VECM.

Delineating energy consumption behaviour: A household-level assessment from India's energy NEXUS strategy

The UN's seventh Sustainable Development Goal aims to ensure everyone affordable and reliable modern energy services by 2030. Despite government efforts, 660 million Indians are still found to be using solid fuels. Exposure to indoor air pollution results in numerous health complications and premature deaths. Against this backdrop, the present study aims to (a) provide an inclusive assessment of cooking energy consumption behaviour and (b) analyse household energy use perception and the scope of LPG as a clean energy source. The study uses 2018 Access to Clean Cooking Energy and Electricity – Survey of States data. It is found that while government schemes are effective in improving penetration, refilling cost of LPG and accessibility remain the principal hurdles. While fear attached with safety issues is found to be a dominant-negative determinants of LPG adoption, better health outcomes and user convenience are found to be major positive determinants associated with LPG use. Free availability, easy accessibility, and other benefits of solid biomass make it even impossible to eliminate fuel-stacking. It is suggested that provision of instant subsidies along with availablity of alternative income-generating opportunitie can reduce the burden of LPG refilling costs on households. Further, making provisions for the users to refill cylinders from the nearest authorised distributors could be a step forward to enhance LPG use. Effort towards promoting energy-efficient alternative technologies (e.g. solar cookers, kettles, etc.) and education, organisation of training programs with the help of local dealers and public institutions, and empowering women can go a long way to boost LPG use.

The role of culture in advancing sustainable energy policy and practice

This paper offers a culturally sensitive analysis of measures to improve the energy performance of households. Comparing and combining insights from two recent sustainable energy initiatives in Ireland, we show how integrated approaches that work with participants’ everyday practices and their links with people and place can yield substantial and sustained reductions in household energy use. Recognizing the centrality of culturally rooted meanings related to domestic energy use and the services it provides, our analysis challenges approaches to energy saving that overemphasize technology use and individual-level behavior change. Instead, it treats domestic energy use as an inherently social phenomenon that reflects place-specific cultural practices and material conditions. The paper also provides important lessons for upscaling and diffusion of good practice that recognize the centrality of formal and informal community networks and related aspects of trust in local social relations. Involving local advocates with strong social awareness and (inter-)cultural competences can increase engagement in community-based initiatives and deliver more sustainable results. Overall, the paper addresses a major energy policy challenge that has generated intense public debate, namely how to design and implement sustainable energy initiatives that meet the needs of participants and deliver lasting reductions in domestic energy use.

Effective policies for reducing household energy use: Insights from Norway

In Norway, stationary energy use in the housing sector grew steeply from the 1970 s to the beginning of the 1990 s, however over the last decades it has reached a saturation level. This article explains why Norway energy use increased at a different rate from 1990 to 2019 (0.15 percent per year) compared to development from 1970 to 1990 (2.4 percent per year). It asks: What explains the actual change in energy use since 1990? How might the choice of different mixes of energy policy measures affect household energy use towards 2030? A complex dynamic model was developed to understand the factors affecting stationary energy use in the housing sector and our analyses highlight three variables that explain the shift in trend: (1) lower growth in in dwelling area per person, (2) lower energy use per m2 and (3) changes in outdoor temperature. Our study helps examine changes in overarching policy during the period from growth towards reducing stationary energy use in the housing sector. In Norway, the goal is to reduce energy use by 10 TWh in existing buildings by 2030, compared with the 2016 level. Should the housing sector take its share this imply a 5 TWh reduction by 2030. To achieve a reduction of 5 TWh by 2030, we conclude that we need to utilize very different policy instruments. The living area per person need to decline and a redistribution of area from detached houses to apartment blocks, thus indicating that an effective policy will be maximum standards for housing consumption per capita. Renovation of existing buildings will also have a major effect and should be applied to reduce gross heating demand and energy for water heating, thus instruments to create supply and demand and new business opportunities for renovation and energy efficiency should be promoted.

Multiple aspects of energy poverty are associated with lower mental health-related quality of life: A modelling study in three peri-urban African communities

ObjectiveOver 900 million people in sub-Saharan Africa (SSA) live in energy poverty, relying on cooking polluting fuels (e.g. wood, charcoal). The association between energy poverty and mental/physical health-related quality of life (HRQoL) among women in SSA, who are primarily tasked with cooking, is unknown. MethodsFemales (n ​= ​1,150) from peri-urban Cameroon, Kenya and Ghana were surveyed on their household energy use and mental/physical health status using the standardized Short-Form 36 (SF-36) questionnaire. Random effects linear regression linked household energy factors to SF-36 mental (MCS) and physical component summary (PCS) scores. A binary outcome of ‘likely depression’ was derived based on participants' MCS score. Random effects Poisson regression with robust error variance assessed the relationship between household energy factors and odds of likely depression. ResultsThe prevalence of likely depression varied by a factor of four among communities (36%-Mbalmayo, Cameroon; 20%-Eldoret, Kenya; 9%-Obuasi, Ghana). In the Poisson model (coefficient of determination (R2) ​= ​0.28), females sustaining 2 or more cooking-related burns during the previous year had 2.7 (95%CI:[1.8,4.1]) times the odds of likely depression as those not burned. Females cooking primarily with charcoal and wood had 1.6 times (95%CI:[0.9,2.7]) and 1.5 times (95%CI:[0.8,3.0]) the odds of likely depression, respectively, as those primarily using liquefied petroleum gas. Women without electricity access had 1.4 (95%CI:[1.1,1.9]) times the odds of likely depression as those with access. In the MCS model (R2 ​= ​0.23), longer time spent cooking was associated with a lower average MCS score in a monotonically increasing manner. In the PCS model (R2 ​= ​0.32), women injured during cooking fuel collection had significantly lower (−4.8 95%CI:[-8.1,-1.4]) PCS scores. ConclusionThe burden of energy poverty in peri-urban communities in SSA extends beyond physical conditions. Experiencing cooking-related burns, using polluting fuels for cooking or lighting and spending more time cooking are potential risk factors for lower mental HRQoL among women.

Will China's household coal replacement policies pay off: A cost-benefit analysis from an environmental and health perspective

To improve air quality in northern China, especially in the Beijing-Tianjin-Hebei (BTH) region, the Chinese government has issued a series of coal replacement policies for clean heating in households in the region. However, little research evaluates the overall costs and benefits and the health co-benefits of the implementation of such policies. Analysis of the results of top-down policies rarely focuses on actual changes in the behavior of subjects at the micro level, leading to a divergence between policy design and evaluation. To quantify the actual implementation effects of China's coal replacement policies in the BTH region, this paper explores the cost-effectiveness of these policies. To this end, we combine the results of a micro survey on household energy use behavior with a policy scenario analysis, take household size into account, and evaluate the environmental cost-benefits and the health co-benefits of clean heating. The results show the implementation of coal replacement policies can significantly reduce CO2, SO2 and NOx emissions. And the cost-benefit analysis demonstrates the cost-effectiveness of China's coal replacement policies. Specifically, during the 13th Five-Year Plan period (2016–2020), by improving air quality in the BTH region, these clean heating policies brought cumulative health benefits of 36.73 billion yuan. Therefore, we conclude that China's clean heating policies can effectively reduce pollutant emissions and have health co-benefits. As we analyze the contribution of different alternative technologies to clean heating and take household size into account, our results can help policymakers formulate technology selection and subsidy policies.