Can industrial agglomeration affect biodiversity loss?

Based on panel data from 16 prefecture-level cities in Shandong Province from 2011 to 2020, the paper utilizes the super-efficiency SBM model with undesirable outputs to measure the green economic efficiency of each city.Spatial autocorrelation analysis and the natural breaks method are applied to analyze the spatial-temporal evolution of green economic efficiency.Lastly, a panel Tobit model is used to analyze the factors affecting green economic efficiency.The study's outcomes are as outlined below: (1) The green economy efficiency of the 16 cities in Shandong Province showed an overall increasing trend from 2011 to 2020.However, there is a noticeable disparity in green economic efficiency among cities, with developed cities exhibiting relatively higher levels of efficiency.(2) The proportion of cities with high green economic efficiency steadily increases, and these highefficiency regions gradually cluster around the provincial capital and the eastern coastal areas.While there is heterogeneous clustering of green economic efficiency, the degree of this heterogeneity decreases over time.(3) Social security, economic development, and technological advancement significantly enhance green economic efficiency, whereas the industrial structure noticeably impedes efficiency.Environmental regulations and urbanization levels have a less pronounced impact on efficiency.Drawing from these findings, this chapter presents targeted policy recommendations.

Under the constraints of energy and environment, improving green economic efficiency (GEE) has become the key path to promote the sustainable economic development. Among the driving factors of GEE, the role of outward foreign direct investment (OFDI) is worth exploring. In this paper, we adopt the inter-provincial panel data of China from 2011 to 2019 and System Generalized Method of Moments (SYS-GMM) to explore the influence of OFDI on GEE. We find that OFDI significantly improves China's GEE, and reverse technology spillover through direct investment in developed countries is an important way for OFDI to promote GEE. Regional heterogeneity test shows that OFDI significantly promotes GEE in eastern China; however, the promotion effect is not significant in midwestern China. Besides, the promoting effect of OFDI on GEE has been further improved after 2016. We further adopt panel threshold model and find that when the financial development (FD) and human capital (HUM) exceeds 2.0954 and 0.0290, respectively, the promoting effects of OFDI on GEE are greatly enhanced. We suppose that the above conclusions can provide guidance for policymakers to optimize OFDI and improve GEE.

Can industrial agglomeration achieve the emission-reduction effect?

How local officials moderate the spatial impact of politically and economically driven environmental regulations on green economic efficiency?

The effect of industrial agglomeration on China’s carbon intensity: Evidence from a dynamic panel model and a mediation effect model

Under the pressure of environmental deterioration, green economic development has become an effective way for countries to solve economic and environmental problems, and scientific assessment of green economic efficiency is important to realize sustainable development. In this study, based on the panel data of 30 Chinese provinces from 2011 to 2022, we first used the super efficiency SBM model to assess the green economic efficiency of each province. Then, we analyzed the spatial and temporal evolutional characteristics of the green economic efficiency by using methods such as kernel density estimation and Theil index. Finally, we used the geographically and temporally weighted regression model to analyze the issue of provincial-level heterogeneity of the influencing factors. The results of the study showed that China's overall green economy efficiency demonstrated an upward trend but with low values, and a shift was observed from multilevel differentiation to polarization. Spatially, it showed a contiguous block distribution with notable east-west differences. Economic development had the greatest impact on green economic efficiency, and the positive impacts of economic development, population density, and financial expenditure on green economic efficiency were more obvious, while the negative impacts of trade openness, energy intensity, and environmental regulation were more obvious.

Green economy is environmentally friendly economy, which is important for the sustainable economic development and environmental protection. Based on the panel data of 30 provinces and cities in China, a three‐stage super‐efficient SBM‐DEA (slack‐based model‐data envelopment analysis) model is constructed to evaluate the efficiency of green economy and analyze its regional differences. The results show that, first, the random error factor and external environmental conditions significantly affect the efficiency of green economy. Second, the green economic efficiency in China from 2010 to 2019 is stable and needs to be further improved. Third, through regional comparison, it is found that the green economy efficiency in eastern China is higher than those in central and western China and the green economy efficiency in northeast China is the lowest. Finally, green economy efficiency does not simply depend on the economic development, the regional differences of green economy efficiency results from the combined effects of different geographical resources endowment, different economic development characteristics, and different environmental protection policies in different regions of China. Based on the research findings, corresponding policy suggestions are put forward to improve the efficiency of green economy.

Digital–intelligent integration (DII) has emerged as a pivotal driver for high-quality urban development, offering a pathway to overcome pressing resource and environmental constraints. By harnessing data as a core production factor and integrating advanced intelligent technologies, DII can substantially elevate urban green economic efficiency (GEE). This study constructs a quasi-natural experiment using the staggered rollout of national big data comprehensive pilot zones (initiated in 2012) and smart-city pilot programs (from 2016 onward). Employing a rigorous staggered difference-in-differences (DID) estimator on panel data from 279 Chinese prefecture-level cities over 2010–2021, we find that DII causally increases GEE by 5.03 percentage points (p < 0.01). This benchmark result remains robust across a comprehensive set of checks, including parallel-trend validation, placebo tests, double/debiased machine learning, two-stage least squares with historical IT-sector instruments, and controls for overlapping policies (e.g., ETS, low-carbon pilots, green finance zones). Mechanism analysis, conducted via a sequential 2SLS control-function approach with lagged mediators and Sobel–Goodman mediation tests, reveals three theoretically grounded channels: (i) enhanced urban ecological resilience (mediates 62%, z = 4.68), (ii) accelerated green technological innovation (55%, z = 4.12, measured by IPC/Y02 patent share), and (iii) heightened entrepreneurial vitality (58%, z = 4.39, new firms per 10,000 residents). Heterogeneity tests show pronounced effects in growing and mature resource-based cities (+1.21% and +11.21%), high-fintech cities (+11.35%), and high-river-density areas (+10.29%) but insignificant impacts in declining resource-exhausted cities (joint F p = 0.08). This study makes four key contributions: (1) it innovatively constructs a continuous DII policy variable by exploiting the synergistic timing of dual pilots, thereby overcoming the limitation of analyzing policies in isolation; (2) it opens the “theoretical black box” by integrating institutional theory and information economics into a unified conceptual framework that explicitly links DII to GEE through reduced transaction costs and alleviated information asymmetry; (3) it enriches the mediation identification strategy in staggered settings using 2SLS control functions and sequential G-estimation, addressing endogeneity in intermediary variables more rigorously than traditional three-step approaches; and (4) it delivers nuanced evidence on the contextual conditions (when and where) under which DII yields the strongest green dividends, providing actionable guidance for China’s “dual-carbon” goals and the global green transition.

Environmental efficiency evaluation is an effective way to assess the synergetic development degree between the economy and environment. In order to realize the mechanism of the effects of urban sprawl and industrial agglomeration on environmental efficiency by using the super efficiency Slacks-based Measure (SBM) model with undesirable outputs, this paper firstly calculates the environmental efficiency of the 13 cities in the Beijing–Tianjin–Hebei urban agglomeration from 2006 to 2017. Then, based on the panel data of the 13 cities, we empirically examine the influence of urban sprawl and industrial agglomeration on environmental efficiency by adopting the panel Tobit regression model. The results show that large gaps exist in the degree of the cities’ environmental efficiency in the research period, and only Beijing’s environmental efficiency exhibits full efficiency in the 13 cities, with the overall environmental efficiency of the Beijing–Tianjin–Hebei urban agglomeration presenting a decreasing trend. Additionally, the Tobit regression results indicate that urban sprawl has a significantly negative impact on environmental efficiency, but with the continual improvement of industrial agglomeration, the negative effects of urban sprawl will be partially offset. From the regression results of control variables, economic growth and urbanization developments have an inhibiting effect on the promotion of environmental efficiency. Furthermore, industrial structure optimization and technological innovation are helpful to improve environmental efficiency. The opening-up policy has had a significantly positive impact on environmental efficiency, and the “pollution paradise” hypothesis was untenable in the Beijing–Tianjin–Hebei urban agglomeration.

External technology sourcing and industrial agglomerations are important development factors and location characteristics of high-tech industries in the context of open innovation. Based on interprovincial panel data on China's high-tech industries from 2009 to 2014, this paper explores the effects of external technology sourcing and industrial agglomeration on innovation performance from the perspective of development trends and regional heterogeneity. The results show significant spatial heterogeneities in the external technology sourcing and industrial agglomeration of high-tech industries. Both external technology sourcing and industrial agglomeration have significant and positive influences on innovation performance. However, the synergistic effect of external technology sourcing and industrial agglomeration on innovation performance is significantly negative, and the positive synergistic effect is not fully reflected. This paper concludes with a discussion of the implications of these insights for innovation management and policy.

International trade has emerged as a threat to biodiversity. Identifying the biodiversity loss in countries/regions driven by international trade is a prerequisite for effective protection. Considering the lack of existing research, it is urgently necessary to construct an equitable and reasonable accounting scheme for biodiversity loss. Therefore, based on the multi-regional input–output model framework, this study uses the International Union for Conservation of Nature Red List of Threatened Species data to evaluate and compare the biodiversity footprints of 188 countries/regions between 2006 and 2015. We find that the world’s biodiversity footprint is characterized by a significant spatial non-equilibrium driven by developed countries and developing countries with large economies. Financial intermediation and business activities, education, health, and other services, and construction and agriculture sectors play an important role in the accounting scheme for biodiversity and hence must be strictly controlled. This study will help construct a biodiversity conservation framework in the 2020s.

Industrial agglomeration and CO2 emissions: Evidence from 187 Chinese prefecture-level cities over 2005–2013

Spatial agglomeration of high-pollution factories and PM2.5 pollution: Evidence from prefecture-level cities in China from 1998 to 2013

Within the framework of China’s socioeconomic transition from a phase of rapid growth to one of high-quality development, it becomes crucial to focus on advancing the green economy to sustain economic progress. Green finance plays a pivotal role in underpinning green industries and fostering the progression of a green economy. Under the auspices of green finance, social capital is increasingly directed towards industries that prioritize energy efficiency, low carbon emissions, and environmental friendliness, thereby spurring technological innovation and industrial metamorphosis in businesses. In this paper, data envelopment approach (DEA) is used to measure the green economic development efficiency of 30 Chinese provinces from 2008 to 2018 in general and by sub-region. The two-way fixed-effects model (Two-way FE) and difference-in-difference (DID) model are established to empirically analyze the effect of green finance on green economy development efficiency and the influence of green polices on these two, with sub-region models examining the heterogeneity of this impact in eastern, western and central regions. The findings suggest that green finance significantly enhances a positive influence on green economic development efficiency, albeit with regional variations. Furthermore, the implementation of green policies facilitates green finance and green economic development.

The Yangtze River Delta (YRD) region has been a strong innovation capability and has a good economic development and innovation environment. This paper will focus on the spatial effects of high-tech industrial agglomeration in the Yangtze River Delta region based on the knowledge spillover theory, with the Yangtze River Delta regional city cluster as the research object. This paper begins with a review of relevant literature on knowledge spillover, high-tech industry agglomeration and the relationship between science and technology innovation and economic growth. On this basis, we measure the degree of industrial agglomeration in 27 urban areas of the Yangtze River Delta using the locational entropy research method and analyze the current situation of innovation and high-tech industry development in the Yangtze River Delta. It is found that most of the urban agglomerations are not randomly distributed in the first and third quadrants, indicating that they have considerable spatial correlation, and then relevant countermeasure suggestions are given.