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Accelerating the green transition of the economy is an effective way to conserve energy and reduce emissions, and its impact on the greenhouse effect deserves in-depth discussion. Based on this, we examine the potential effect of China's green growth on carbon dioxide (CO 2 ) emissions by applying provincial panel data from 2004 to 2018. The regional heterogeneity and how does green finance affect the green growth-CO 2 nexus are also checked. The primary findings imply that: (i) China's green growth achieves preliminary results, and its impact on CO 2 emissions is significantly negative. Also, green finance can facilitate carbon emission reduction; (ii) significant regional heterogeneity exists within various regions. Only in the central and western regions can green growth effectively reduce CO 2 emissions, and in the eastern and central regions, green finance is conducive to promoting carbon reduction; and (iii) the mediating role of green finance is significant. In other words, China's green growth not only mitigates the greenhouse effect directly, but also affects CO 2 emissions indirectly by accelerating the development of green finance.

Chinese society has experienced a dramatic change over the past several decades, which has had a profound impact on its household system. Utilizing the Chinese national census and 1% population survey data from 1982 to 2015, this study demonstrates the transition of the Chinese household structure through typology analyses. Five typical regional household structure types—large lineal, large nuclear, small nuclear, mixed lineal, and small and diverse—are identified. Our findings demonstrate that since the 1980s, the household system in almost all Chinese regions has evolved from a large unitary model to a small diversified one. However, this evolutionary path diverged after 2000 and formed two distinct household structure systems. There are also significant regional differences in the transition trajectory. Influenced by developmental, cultural, and demographic factors, the regions exhibit four distinct transition paths: lineal tradition, nuclear retardation, smooth transition, and fast transition. On the basis of these results, we discuss family modernization and other theories in explaining the transition of the Chinese household structure.

China’s North-South regional differences have been widening since the new century, with obvious differences in the roles of various growth factors. Using the decomposition framework of regional differences based on development accounting and China’s provincial-level data from 1978–2022, this study investigates the impacts of total factor productivity, physical capital, and labor inputs on the economic differences between the North and South, and finds that: (1) the difference in total output between the North and the South has continuously expanded, but the gap of output per worker has not changed much in the last decade or so; (2) total factor productivity differences have been an important factor influencing the North-South differences, and will likely dominate the future trend of regional differences; (3) physical capital differences are more prominently affected by regional policies, but the policy effects need to be coordinated and considered in many ways.

As one of the major sources of carbon emissions, the significant spatial disparities in agricultural carbon emissions (ACE) pose a serious challenge to coordinated regional carbon reduction efforts. In order to precisely identify the sources of these ACE differences, this study estimates the ACE of China from 2005 to 2020 across four main emission sources and applies the bidimensional decomposition method of the Gini coefficient to measure and decompose their spatial disparities. Finally, the key factors driving ACE disparities are analyzed using the Quadratic Assignment Procedure (QAP). The results show that China’s total ACE initially declined, followed by an upward trend over the study period. Spatially, emissions were higher in eastern regions compared to western regions, and higher in southern regions compared to northern regions. The differences in paddy field emissions between the central and western regions were identified as the primary contributor to east–west disparities, while differences in agricultural materials emissions between northern and southern regions were the dominant source of north–south disparities. Furthermore, regional differences in agricultural development levels and mechanization capacity were found to be the strongest drivers of spatial ACE disparities. This study provides empirical evidence for formulating region-specific and source-targeted carbon reduction policies. Our findings highlight the importance of addressing regional imbalances, particularly in paddy field management and agricultural material usage, to promote more coordinated and sustainable agricultural carbon reduction across China.

PurposeThe growth of the Chinese economy has resulted in a significant increase in construction and demolition waste (CDW), and regional differences in CDW generation are gradually increasing. The purpose of this study is to investigate the regional differences in CDW generation and the driving factors that influence CDW generation in different areas of China. To provide a systematic advisement for local governments to select the appropriate policy, reduce CDW generation.Design/methodology/approachThe generation of CDW was calculated by region, based on the area estimation method, from 2005 to 2018. The relationship between CDW generation and economic development, and the driving factors of CDW generation in different regions of China, was investigated using the environmental Kuznets curve (EKC) model and the STIRPAT theoretical model.FindingsCDW generation of China increased at the average annual growth rate of 10.86% from 2005 to 2018. The main areas of CDW generation were concentrated in the eastern and central regions, while the proportion of CDW generation in the northeast region decreased gradually, and the changes varied significantly across different regions. The EKC between CDW generation and economic development was established for the whole country, North China, Northeast China, East China, Central South China, Southwest China and Northwest China. Three main factors based on the STIRPAT theoretical model were identified and explained into a framework to reduce CDW generation. The results provided a useful theoretical basis and data support guide for devising effective policies and regulations for the Chinese context.Practical implicationsThe findings from this study can ultimately support policymakers and waste managers in formulating effective policies for waste management strategies and CDW-specific legislation. Additionally, it can help the coordinated reduction of CDW generation across regions in China and can support construction enterprises (in their development strategies), similar developing economies and foreign firms planning to operate in China.Originality/valueThis study contributes to the field through the STIRPAT model on driving factors of CDW generation in the Chinese context, in different regions.

Green manufacturing has become a necessary way to promote new industrialization and realize the high-quality development of China’s manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2022, this paper constructs a comprehensive evaluation index system for the green manufacturing development level and introduces the TOPSIS- Gray correlation method to comprehensively measure the green manufacturing development level of China as a whole and the four major regions in the eastern, central, western, and northeastern parts of the country. The regional differences, distribution dynamics and evolutionary trends of China's green manufacturing development level are also explored with the help of the Gini coefficient, kernel density estimation and Markov chain methods. Research Findings: (1) The green manufacturing development level in China is on an upward trend, with an overall spatial distribution pattern of “East is superior and West is inferior”. (2) There are regional differences in the green manufacturing development level in China, and the differences are widening, with interregional differences being the main reason for this overall difference. (3) The country as a whole, the central region and the western region are polarized to varying degrees, with the rest of the country showing an improvement in polarization. (4) Without considering spatial factors, the development of green manufacturing in each province experiences “club convergence” in the short term, and it is difficult to realize rapid development. Considering spatial factors, China's green manufacturing development level is generally characterized by “elevated in proximity to high levels and suppressed in proximity to low levels”, and in the long run, it shows a distribution trend toward the concentration of high values. The findings of this study can provide new ideas for promoting synergistic efficient development of green manufacturing in China.

A wealth of recent research in comparative politics examines how spatial variation in historical conditions shapes modern political outcomes. In an article in the American Political Science Review, Homola, Pereira, and Tavits argue that Germans who live nearer to former Nazi concentration camps are more likely to display out-group intolerance. Clarifying the conceptual foundations of posttreatment bias and reviewing the historical record on postwar state creation in Germany, we argue that state-level differences confound the relationship between distance to camps and out-group intolerance. Using publicly available European Values Survey data and electoral results from 2017, we find no consistent evidence that distance to camps is related to contemporary values. Our findings have implications for literatures on historical persistence, causal inference with spatial data, Holocaust studies, and outgroup tolerance.

This paper analyzes the current situation of the development of tourism in Hainan and provides a new reference for the high-quality development of Hainan tourism. Firstly, the evaluation model and evaluation indexes of Hainan tourism high quality development based on a comprehensive evaluation method are constructed, the comprehensive index method is used to dimensionless process the data, and the entropy value method is used to conduct a comprehensive analysis of the quantitative relationship of selected indexes and the degree of variation to determine the index weights. Then the multi-objective linear weighting method is used to calculate the comprehensive level of Hainan tourism quality development. Then the regional difference characteristics of Hainan tourism quality development and the trend of tourism development time sequence evolution were analyzed, and finally, the path of Hainan tourism quality development was analyzed from multiple factors. On the regional difference characteristics, the northern and southern regions of Hainan have obvious advantages in terms of quality tourism development, with a comprehensive development index of 0.498 and 0.482 respectively, while the central region is relatively backward, with a development index of 0.364. The dynamics of industrial development in Hainan are relatively low, with slow and unstable growth, rising only from 0.0048 in 2013 to 0.0059 in 2018 and also showing a small downward trend in 2015 and 2016. This study has important implications for the high-quality development of tourism in Hainan.

Amid the Fourth Industrial Revolution and the 2030 Sustainable Development Goals (SDGs), China’s digital village initiative has emerged as a localized implementation for achieving multidimensional sustainability. However, the progress of digital villages in China remains uneven, posing challenges to achieving sustainable rural transformation. This study develops a multidimensional index system at four levels: rural digital infrastructure, the digital development environment in rural areas, the digital industry in rural areas, and agricultural production digitalization. Entropy weighting was used to evaluate digital village progress across 30 Chinese provinces (2013–2022). Kernel density estimation, the Dagum Gini coefficient, and the obstacle degree model were used to study China’s spatiotemporal dynamics, regional disparities, and digital village development barriers. The results show that between 2013 and 2022, digital villages in China advanced (the average annual growth rate: 9.43%), with a spatial distribution pattern of “east superior, west inferior, south prosperous, and north declining”. National and regional digital villages have advanced yearly, with absolute and relative disparities increasing, extensibility increasing, and multi-polarizing rising. Digital village development is becoming increasingly imbalanced, with inter-regional differences driving “east, central, and west” disparity and intra-regional disparities driving North–South disparity. Ranking the average hurdle levels: the digital industry in rural areas (45.94%) > the digital development environment in rural areas (24.83%) > rural digital infrastructure (21.85%) > agricultural production digitalization (7.38%). Taobao villages are a major restraint on China’s digital village development.

This paper constructs a comprehensive evaluation system of agricultural new quality productivity (ANQP) covering three levels: technology, organization, and environment, and is refined into five dimensions: new quality laborers, new quality means of production, new quality labor objects, new quality development methods, and new quality development environment, based on the TOE framework. Using panel data from 31 provinces in China from 2011 to 2021, the entropy method, Dagum Gini coefficient, kernel density estimation, and Markov chain are used to measure the development level of ANQP, and its regional distribution pattern and dynamic evolution characteristics are deeply analyzed. Founding that: (1) Although there are differences in the development level and growth rate of AANQP in various provinces in China, they are generally on a continuous upward trend. High-level regions play a leading role, while medium and low-level regions accelerate their catch-up, jointly promoting the evolution of the national development pattern towards a balanced direction. (2) The development level of ANQP in the nine major agricultural regions shows a significant gradient distribution, and regional differences are the main factor leading to overall unbalanced development. (3) There is a structural imbalance in dimensional development, with obvious differentiation of regional advantages. Different regions have their characteristics and shortcomings in specific dimensions. In terms of hierarchical development, the organizational level has the highest level of development, and there is still room for improvement in the levels of technology application and environmental optimization; (4) The spatial distribution of ANQP has converged from a multi-peak model to a single-peak model, and regional differences have narrowed, but the overall level is still low. There is a \"club convergence\" effect between regions. The development level of each province is highly sustainable, and it is difficult to upgrade across stages, but the upward transfer trend is significant.