Examining the spatiotemporal variations and inequality of China's provincial CO2 emissions

Inequality characteristics and influencing factors of CO2 emissions per capita in Jiangsu Province, China

Analyzing the inequality characteristics and influencing factors of CO2 emissions per capita (CEPC) is conducive to balancing regional development and CO2 emissions reduction. This study applied the Gini coefficient and Theil index to investigate the CEPC inequalities during 2005-2017 at the county level in Jiangsu Province, China. Considering the spatial spillover and interaction effects, the factors influencing CEPC were analyzed by a hierarchical spatial autoregressive model. The results showed that the inequalities in CEPC first increased and then decreased at the inter-regional, and inter-county levels. The spatial pattern of CEPC was stable, and there was a significantly positive spatial autocorrelation of CEPC at the county level. The High-High type counties were mainly located in Sunan (southern Jiangsu). The spatial interaction effects of the CEPC between the prefecture and county levels indicated that governments at the prefecture level should integrate their county governments to reduce the CEPC. Moreover, carbon intensity, GDP per capita, land urbanization, and industrial structure play an important role in reducing CEPC. Our findings provide a scientific basis for formulating reasonable and effective carbon emission reduction policies.

A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies.

Can industrial co-agglomeration improve carbon emission efficiency? Empirical evidence based on the eastern coastal areas of China

The goal of "carbon peak and carbon neutrality" is the key to coping with global warming and achieving high-quality development. Producer services and manufacturing co-agglomeration (Coagglo) is an important path to achieve low-carbon development. Therefore, the relationship between industrial co-agglomeration and carbon emission efficiency (CEE) needs to be discussed. Based on the panel data of 114 cities along the eastern coast of China from 2006 to 2021, this study uses a panel quantile regression model and dynamic spatial Durbin model to evaluate the impact and spatial effect of Coagglo on CEE. The results show that there is a nonlinear relationship between Coagglo and CEE. When it exceeds the 50th quantile, the degree of influence decreases slightly, but it still shows a significant positive correlation. When considering industry heterogeneity, we find that the co-agglomeration of warehousing and postal industry (TRA) and manufacturing has the most significant impact on CEE, while the co-agglomeration of leasing and commercial service industry (LEA) and manufacturing has the least impact on CEE. Regional heterogeneity shows that the Coagglo has a greater impact on carbon emission efficiency in the northern region than in the southern region. In addition, Coagglo promotes the spillover of knowledge and technology and has a positive spatial spillover effect on CEE. This conclusion provides a theoretical reference for carbon emission reduction in eastern coastal areas of China.

Regional differences and driving factors of carbon emission intensity in China's electricity generation sector

As an industry with immense decarbonization potential, the low-carbon transformation of the power sector is crucial to China's carbon emission (CE) reduction commitment. Based on panel data of 30 provinces in China from 2000 to 2019, this research calculates and analyzes the provincial CE intensity in electricity generation (CEIE) and its spatial distribution characteristics. Additionally, the GTWR model based on the construction explains the regional heterogeneity and dynamic development trend of each driving factor's influence on CEIE from time and space. The main results are as follows: CEIE showed a gradual downward trend in time and a spatial distribution pattern of high in the northeast and low in the southwest. The contribution of driving factors to CEIE has regional differences, and the power structure contributes most to the CEIE of the power sector, which promotes regional CE. Concurrently, most provinces with similar economic development, technological level, geographic location, or resource endowment characteristics show similar spatial and temporal trends. These detections will furnish broader insights into implementing CE reduction policies for the regional power sector.

Per capita CO2 emission inequality of China's urban and rural residential energy consumption: A Kaya-Theil decomposition

With the increasing affluence, the differences in CO₂ emission between urban and rural residential sectors are remarkable and show an increasing trend. In case of China, residential sector accounts for a substantial share of the national CO₂ emission, bringing greater pressure to achieve the goal of carbon peak. Analyzing the emission inequality trend and its drivers is essential for formulating effective CO₂ emission reduction policies. However, the existing literature lacks relevant analysis from the viewpoint of urban-rural disparity. Hence, this study decomposes the CO₂ emission inequality of China's urban and rural residential consumption into four factors by combining the Theil index and Kaya decomposition. The results suggest that, in 2005-2020, the per capita CO₂ emission of rural residential consumption increased to a higher extent than those of urban households, with large differences in spatial distribution. Decomposition of the per capita CO₂ emission inequality for residential sector shows that the primary source is the inequality within the groups, mainly from the urban intra-group inequality. Based on the static decomposition, energy intensity appears as the main factor of urban-rural inequality. The dynamic decomposition shows that there have been differences in the factors of the change in the Theil index between urban and rural areas across sub-periods.

Association Between Carbon Emission and Low Birth Weight in Mainland China

OBJECTIVE

The aim of this study is to investigate the relationship between carbon emission and low birth weight (LBW).

METHODS

A nested case-control study was contacted in mainland China. Multilevel logistic regression was used to estimate the effect of carbon emission on LBW. Generalized additive mixed effect model was performed to assess no-linear trend between LBW and carbon emission.

RESULTS

Carbon emission was a risk factor for LBW (odds ratio, 1.182; 95% confidence interval, 1.011-1.383). Carbon emissions from power, residence, aviation, and transport department were risk factors for LBW (all P < 0.05). Moreover, generalized additive mixed effect model has shown that the risk of LBW decreased first and then increased as carbon emissions increased.

CONCLUSIONS

Our study initially found that carbon emission may be a risk factor for LBW.