Spatioethnic Household Carbon Footprints in China and the Equity Implications of Climate Mitigation Policy: A Machine Learning Approach

Abstract

This article relies on the first and only representative survey data to estimate household carbon footprints (CFs) of China’s large yet vastly understudied ethnic minority population, documenting for the first time significant ethnic disparities in CFs driven by ethnic minorities’ relatively worse-off living standards: From 2010 to 2020, China’s ethnic minority population contributed less than 6 percent of residential emissions, about 50 percent less than expected based on population share alone. Next, results from a counterfactual policy analysis find that the distributive effects of a carbon tax are regressive in urban areas but not in rural areas, increasing within and between ethnic group inequality in urban China. A carbon tax with revenue-neutral schemes, by contrast, helps to mitigate existing inequalities in society, reducing income- and ethnic-based forms of inequality. Results are robust to machine learning techniques employed to simulate potential heterogeneous household abatement scenarios. The findings emphasize the potential benefits of a carbon tax, contributing to a more comprehensive understanding of climate justice and informing policy decisions that promote equitable outcomes for vulnerable segments of society.

基于第一个也是唯一的代表性调查数据, 本文估计了大量但鲜有研究的中国少数民族家庭的碳足迹, 首次记录了少数民族较低生活水平所导致的碳足迹的显著民族差异。从2010年到2020年, 中国少数民族人口对住宅排放的贡献不到6%；按人口比例计算, 比预期值低约50%。其次, 反事实政策分析发现, 碳税的分布式效应在城市地区是递减的, 但是在农村地区并非如此, 这在中国城市地区加剧了民族内部和民族之间的不平等。收入中性碳税有助于缓解社会不平等、降低收入和种族不平等。基于机器学习的不同家庭减排场景模拟具有鲁棒性。研究结果强调了碳税的潜在作用: 有助于更全面地理解气候正义、促进社会弱势群体公平的政策决策。

Este artículo se basa en los primeros y únicos datos de encuestas destinados a calcular las huellas de carbono (CFs) de hogar en la tan numerosa como mínimamente estudiada población de minorías étnicas de China, documentando por primera vez las significativas disparidades étnicas en CFs determinadas por el nivel de vida relativamente más bajo de las minorías étnicas: De 2010 a 2020, la población de las minorías étnicas de China contribuyó con menos del 6 por ciento de las emisiones residenciales, algo así como el 50 por ciento menos de lo anticipado basado tan solo en la contribución de la población. Luego, los resultados de un análisis contrafactual de las políticas encontraron que los efectos distributivos de un gravamen tributario son regresivos en las áreas urbanas, pero no en las rurales, aumentando la inequidad dentro y entre los grupos étnicos en la China urbana. Al contrario, un impuesto al carbono con sistemas de ingresos neutros ayuda a mitigar las desigualdades existentes en la sociedad, reduciendo los tipos de desigualdad basados en ingreso, y en los orígenes étnicos. Los resultados son robustos frente a las técnicas de aprendizaje automático usadas para simular en los hogares posibles escenarios heterogéneos de supresión. Los resultados enfatizan los beneficios potenciales de un impuesto del carbono, contribuyendo a un entendimiento más amplio de la justicia climática e informando las decisiones políticas que promueven resultados equitativos para los sectores vulnerables de la sociedad.

关键词:

• 碳足迹
• 气候缓解政策
• 民族不平等
• 机器学习

Palabras clave:

• aprendizaje automático
• desigualdad étnica
• huella de carbono
• políticas de mitigación climática

Key Words:

• carbon footprint
• climate mitigation policy
• ethnic inequality
• machine learning

Disclosure Statement

No potential conflict of interest was reported by the author.

Notes

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/24694452.2024.2313496

Notes

1 For a recent review of the literature on carbon pricing, see Haites (2018).

2 Carbon pricing initiatives cover less than 15 percent of annual global greenhouse gas emissions (Bank 2020).

3 See Ohlendorf et al. (2021) for a review on the distributive effects of carbon mitigation schemes.

4 The distributional effects of a carbon tax are found to be regressive or progressive in China and other developing country contexts, depending on policy design and local factors (Brenner, Riddle, and Boyce 2007; Parry et al. 2016; Dorband et al. 2019).

5 Between-ethnic-group inequality is linked to various forms of social instability (Olzak 1994).

6 Other carbon pricing schemes exist in China; for example, an emission trading system (Cui et al. 2021).

7 The carbon dividend is a cross-regional transfer of assistance that gets redistributed as an equal lump-sum payment for all rural and urban individuals.

8 The Dibao program is a national means-tested social assistance program targeting the poor, one of the largest minimum income guarantee programs in the world (Kakwani et al. 2019).

9 The glaring absence of ethnic CF reflects the broader lacuna of Han–minority empirical evaluations, historically due to the lack of suitable microdata.

10 In more recent years, a limited number of studies relying on CHES document ethnic-based disparities along socioeconomic dimensions (e.g., wages, income, and mobility; Myers et al. 2013; Howell 2017, 2020).

11 For more details about CHES and sample design, see Gustafsson, Hasmath, and Ding (2020).

12 See Cowell (2011) for an overview of the development of the Theil-L general entropy measure and its decomposition.

13 As an example, reducing the emission level to the observed 2011 level is analogous to lowering the tax amount because both actions effectively reduce the tax burden and therefore the incentive for carbon reduction. Adjusting either of these parameters leads to comparable effects on households’ behavior and the overall effectiveness of the carbon tax policy.

Additional information

Notes on contributors

Anthony Howell

ANTHONY HOWELL is Associate Professor and Director of the Center on Technology, Data and Society in the School of Public Affairs at Arizona State University, Tempe, AZ 85004. E-mail: [email protected]. His research interests include economic development, economic geography, sustainable development, and computational social science.