The challenges of rising income on urban household carbon emission: do savings matter?

doi:10.1016/j.jclepro.2021.129295

Journal of Cleaner Production

Volume 326, 1 December 2021, 129295

https://doi.org/10.1016/j.jclepro.2021.129295 Get rights and content

Highlights

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The heterogeneity of household income have an important impact on the carbon-neutral transition.
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An increase in savings enables clean energy consumption of middle-saving residents.
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High-savings households have high sensitivity to heating energy consumption.
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Climate policy should incorporate the carbon spatial transfer and regional inequality of household electricity consumption.

Abstract

As an essential household economic factor, household savings with increasing income affect household energy access. However, there is limited understanding of the household savings accumulation differs the impact of income on household energy and related CO₂. Based on the panel data of 284 cities in China, a threshold regression model is constructed to investigate the non-linear effects of income on urban household carbon emissions at different saving levels. We found that the heterogeneity of residential savings substantially mattered the income effect on urban household carbon emission. Especially, low-saving households struggled to obtain clean energy, needed more government intervention to replace the fossil fuel system with a clean household energy system. By comparison, middle- and high-saving households could easily transit towards a clean energy system, including natural gas and electricity. Moreover, residential income strongly drives the rising household electricity and related CO₂ at the upper-middle saving level. High-saving households showed high sensitivity to heating energy in northern China. In addition, an in-depth analysis of challenges faced by the energy consumption of Chinese urban households in coping with climate change is carried out, and policy recommendations in most undeveloped regions of China and other counties with high-carbon energy structures are put forward accordingly.

Introduction

As one of the world's significant carbon dioxide emissions (CO₂) (British Petroleum, 2021), China has been actively attempting to control its carbon emissions. The Chinese government also announced a carbon neutrality target of 2060 with the most effort in 2020 (Xinhua News Agency, 2020).

For a long time, the focus of China's carbon reduction task has been on the urban industrial sector. However, as industrial energy use technology continues to advance, the scope for its reduction is gradually shrinking. To realize sustainable development, China's economy is presently transforming to a "new normal", characterized by lowering economic growth, upgrading its industrial structures, and converting the motivating force of economic growth from key elements and investment to innovation (Li and Zhang, 2017). Therefore, energy efficiency in the urban household sector has become key to achieving rapid reductions in emissions (Chai and Zhang, 2010).

Household consumption behaviors and patterns have a significant impact on resources and the environment. Therefore, understanding the influencing factors of household energy consumption behavior will play a positive role in achieving carbon peak and carbon neutrality in China (Mallapaty, 2020). Household energy consumption behavior is influenced by a series of factors, including economic factors, living habits, social values and norms, etc., and economic factors are generally considered to be the most effective means (Linden et al., 2006; Ma et al., 2013).

The "Income Doubling Program" was proposed in the report of the eighteenth National Congress of the Communist Party of China in November 2012 and aimed to "double the 2010 GDP and per capita income of urban and rural residents by 2020". The implementation of the program has brought about a rapid increase in Chinese residential income and savings accumulation. Rapid economic development is often accompanied by a widening gap between rich and poor as well as environmental damage (Shin, 2012). The widening residential income gap will likely increase carbon emissions (Dinda and Coondoo, 2006; Wolde-Rufael and Idowu, 2017). Meanwhile, households with different income levels have distinguishing consumption patterns (Yan and Minjun, 2009). This heterogeneity may lead to different impacts on energy consumption and CO₂ emissions. Because of the spatial difference in residential income and savings, the plan might cause the expanding spatial differences in household energy consumption.

Human-induced urban growth and sprawl have implications for greenhouse gas. (Pan et al., 2020). Cities are both population agglomerations and energy-consumption centers. In China, cities could account for approximately 75% of total national energy consumption and 85% of energy-related CO₂ emissions (Dhakal, 2009; Li et al., 2018). Additionally, in 2019 urban resident income was nearly three times that of rural residents in China (China Statistical Yearbook, 2020). By 2020, 60% of the population is expected to live in cities in China (Zhu et al., 2013). Therefore, cities will be the main areas for climate change adaptation and mitigation of energy consumption and implementation of energy-saving and emission reduction policies (Xie and Weng, 2016).

The considerable energy saving potential of the household sector has also attracted increasing attention in recent years to the mechanisms influencing household energy consumption and carbon emissions. However, it has mainly focused on the national, sub-national, or provincial level, and relevant research at the city level is still lacking.

Some studies have quantitatively analyzed and estimated the CO₂ emissions of Chinese cities (Jing et al., 2018; Liu et al., 2018; Mi et al., 2016; Shan et al., 2017). Cai et al. (2019) established a unified prefecture-level city CO₂ emissions inventory for 287 cities nationwide in 2005. However, preliminary research is still on the influence mechanism of a single factor on the Chinese urban household energy-related carbon emissions.

With increasing attention on household carbon emissions, increasing research has been conducted to explore household economic factors on household energy consumption and carbon emissions. These studies were mainly concerned with the effects of income on household carbon emissions. These studies revealed a linear relationship between household income and household carbon emissions in countries such as Spain, France, and the United States (Chancel, 2014; Duarte et al., 2010). In China, Peters et al. (2007) demonstrated that increased household CO₂ emissions are driven by a combination of increased urban household expenditure and urbanization. Wyatt (2013) conducted a statistical analysis using data from a sample survey of household energy consumption in China from 2004 to 2008 and found that income was positively associated with household energy consumption. However, these studies do not distinguish between different income levels and stages of development.

Moreover, recent literature found that the relationship between income and energy consumption/emissions varies across developmental stages or income levels (Madlener and Sunak, 2011; Zhang and Lin, 2012). Golley and Meng (2012) investigated the impact of urban household income inequality on carbon emissions. Feng et al. (2011) argued that income gaps would significantly affect carbon emissions. Liao and Cao (2013) found that countries with a higher income and population density generally emit more CO₂. Poumanyvong and Kaneko (2010) suggested that the impact of urbanization on energy use and emissions varies across the stages of development. Urbanization decreases energy use in the low-income group, increasing energy use in the middle- and high-income groups. Although the non-linear relationship between income and household CO₂ emissions has been explored, the household income is divided by subjective grouping or income level standard in existing researches. Quantitative division usually adopts the method of adding the quadratic terms, which easily produce estimation error.

The knowledge gap is how the accumulation of savings changes the impact of income on household carbon emissions from energy consumption. What are the challenges to climate targets and actions of China? Therefore, this study uses savings as a threshold to explore the non-linear effects of income on urban household carbon emissions at different saving levels. In contrast to previous studies, we (1) mapped household carbon emissions in the 284 Chinese cities; (2) identified the criteria for classifying saving levels through a panel threshold model. The panel threshold model assumes that structural changes are embedded in the economic system and that a scientifically sound judgment can be made to obtain a threshold for economic performance. In addition, this method minimizes the errors associated with the data. Finally, (3) investigated the non-linear effect of income on carbon emissions of urban households at different levels of savings.

Unlike previous studies, this paper is pioneering to explore the non-linear relationship between residential income and household carbon emissions in two aspects. First, considering the significance and heterogeneity of residential savings in China, we build a panel threshold effect model to represent the non-linear effects of income based on the different threshold of saving levels. Besides, the energy heterogeneity impact of household savings with income is novelly explored in this study.

The structure of this study is as follows. Section 2 provides the methodology and a description of the variables and data. Section 3 presents the empirical results. Section 4 discusses the income-CO₂ relationship at different saving levels and challenges for the climate target achievement in China. Finally, Section 5 presents the overall conclusions and policy implications.

Section snippets

Econometric model and estimated strategy

Based on the theoretical analysis of the influence factor of household carbon emission (SI Appendix), household direct carbon emission per capita can be described as a function of per capita household income, per capita household savings balance, the number of households using fuel, per capita household energy consumption, regional population density and regional per capita GDP. To eliminate the heteroscedasticity of the variables, the logarithmic form of the model is used: $\ln P H C E i t = α + β_{1} \ln p i n c o m e$

Stationary test

Supplementary Table 1 presents the correlation matrix for the dependent and independent variables. Household energy-related carbon emissions correlated with all the independent variables at a significance level of p < 0.01. The variance inflation factor (VIF) of each variable was less than 10, indicating zero multicollinearity in the explanatory variables selected for the model and an effective regression analysis (Supplementary Table 2).

Our sample is panel data, which may have problems of

Energy heterogeneity of the income-CO₂ relationship across saving levels

Considering the savings balance (as a time accumulation, with its own periodic changes), the threshold model was applied to identify the threshold value. We determined that the non-linear effect of income on CO₂ shows energy heterogeneity across saving levels.

Conclusions and policy implications

For over a decade, China's rapid urbanization and soaring household energy consumption have compelled policymakers to explore the challenges facing climate targets. To examine the impact of residential income on household carbon emissions at different saving levels, we constructed a threshold model with city panel data to investigate the non-linear effects and policy implications.

Based on the estimated results, we observed that the heterogeneity of household income led to differences in

CRediT authorship contribution statement

Junfeng Wang: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Nana Li: Writing – original draft, data estimation. Mengdi Huang: Writing – review & editing. Yue Zhao: Writing – original draft, data estimation. Yuanbo Qiao: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant numbers 72174097] and the Fundamental Research Funds for the Central Universities.

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The challenges of rising income on urban household carbon emission: do savings matter?

Highlights

Abstract

Introduction

Section snippets

Econometric model and estimated strategy

Stationary test

Energy heterogeneity of the income-CO2 relationship across saving levels

Conclusions and policy implications

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgments

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Appl. Energy

Energy

Ecol. Econ.

Energy Pol.

Ecol. Econ.

Energy Econ.

Econ. Lett.

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Energy

Energy Econ.

Energy Pol.

J. Econom.

Energy Econ.

J. Econom.

J. Clean. Prod.

J. Econom.

Appl. Energy

Renew. Sustain. Energy Rev.

Global Environ. Change

Energy Pol.

J. Clean. Prod.

Energy Pol.

Sustain. Cities Soc.

Energy heterogeneity of the income-CO₂ relationship across saving levels