As the world's largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China's carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path. 

A machine learning algorithm to explore the drivers of carbon emissions in Chinese cities

Enhancing green innovation is the key to realizing high-quality development and the "dual-carbon" goal. This paper takes the data from Chinese cities from 2000 to 2018 as a sample to test the impact and mechanism of local governments' competition for the environment on urban green innovation. The study found that local government competition for the environment can effectively promote urban green innovation, and the spatial spillover effect is significant and can lead neighboring cities to improve the level of green innovation jointly. The results of the mechanism test indicate that local government competition promotes green innovation through two channels: influencing fiscal expenditure bias and the establishment of development zones. Further analysis shows that the impact of government competition on green innovation has apparent spatial heterogeneity, while the double-threshold effect results in an "N"-shaped relationship between local government competition and urban green innovation. 

Local governments competing for the environment and green innovation-evidence from China

The relationship between relation-based governance and economic growth has drawn much academic attention; however, previous studies have not fully recognized the causal effect between them from the perspective of development stages. Based on government–enterprise relations, this study first defines relation-based governance by the days enterprises deal with the government. It takes the shock of the 2008 global financial crisis as a quasi-natural experiment to investigate the impact of relation-based governance on China's economic growth with the generalized difference-in-differences approach. Using panel data of Chinese cities from 2003 to 2018, we find that after the shock of the global financial crisis, as China shifted from an extensive development stage to an innovation-driven development stage, relation-based governance significantly hindered economic growth and cities with a stronger dependence on relation-based governance exhibit decreasing economic growth. Further analysis reveals that the key mechanisms involve excluding rule-based governance and weakening enterprise innovation respectively. 

Relation-based governance, financial crisis shock, and economic growth in China

Which works better? Comparing the multiple effects of heterogeneous environmental regulations on urban green economic transformation in China.

Abstract This study evaluates the efficiency of green economy development (GED) in Northwest China by integrating Data Envelopment Analysis (DEA) and Tobit regression models. Spatial heterogeneity was assessed using global and local Moran’s I indices from 2016 to 2022. Results revealed significant regional disparities, with mean GED efficiency scores ranked as Northern Xinjiang (0.8815) &gt; Southern Xinjiang (0.7656) &gt; Whole Xinjiang (0.7607) &gt; Eastern Xinjiang (0.4711). Tobit regression analysis identified key influencing factors (general public budget expenditure and tourism revenue exhibited negative correlations with GED efficiency). In contrast, student enrollment and total import–export volume showed positive associations. These findings underscore the critical role of human capital development and trade openness in enhancing GED efficiency. The DEA-Tobit framework provides a robust methodological tool for policymakers to formulate targeted strategies in resource-dependent regions, offering scalable insights for similar economies globally. Highlights DEA-Tobit modeling with spatial autocorrelation to assess GED efficiency Prioritize human capital investment and trade facilitation to boost GED efficiency DEA-Tobit framework serves as a versatile template for efficiency analysis Re-evaluate budget allocation strategies to avoid inefficiencies in public spending 

pdf/a-comprehensive-overview-based-on-data-envelopment-analysis-9uz27agma21j.pdf

A Comprehensive Overview Based on Data Envelopment Analysis (DEA): An Approach Towards Green Economy Development and Sustainability

PurposeThis paper explores international trade of the Chinese manufacturing industries through the lenses of network analysis (NA) to visualise the world trade network of the Chinese economy, describe its topology and better explain the international organisation of Chinese manufacturing industries.Design/methodology/approachThe authors built a dataset of 40,550 Chinese companies and their 107,026 subsidiaries in 118 countries from Orbis-BVD and used a NA to investigate the connection between China and other countries. In particular, the authors studied the connections between Chinese companies and their subsidiaries in order to build a network of Chinese industries.FindingsThe authors found that the network of Chinese companies is ramified but not wide and it can be divided into two clusters. Moreover, the relations between China and other peripheral countries are strongly mediated by a few leading locations (e.g. Hong Kong and the USA).Originality/valueThis paper contributes to the literature in several ways. First, the authors provide empirical evidence on the magnitude and ramifications of Chinese enterprises in the world. The existing studies generally focus on applying NA to sectoral insights (Mao and Yang, 2012; Shaikh et al., 2016; Zheng et al., 2016; Wanzenbö ck, 2018; Krichene et al., 2019), whereas in this work the authors take a comprehensive view of the entire Chinese manufacturing system. Second, this paper complements the existing literature identifying the difference between cluster levels in Chinese manufacturing (Wu and Jiang, 2011) by proposing a cluster centralisation method to analyse the international network of Chinese firms rather than just the national network. Finally, the results also shed light on the international trade relationship between China, Hong Kong and the USA.

/pdf/economic-network-dynamics-a-structural-analysis-of-the-1rkjy2qn.pdf

Economic network dynamics: a structural analysis of the international connectivity of Chinese manufacturing firms

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Economic network dynamics: a structural analysis of the international connectivity of Chinese manufacturing firms