Sustainable Development In China Research Articles

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Does Industrial Intelligence Promote Sustainable Employment?

One of the key driving factors for achieving the goal of sustainable economic development is to ensure decent employment opportunities. This article explores the relationship between industrial intelligence and sustainable development in China from the perspective of employment. Based on interprovincial panel data from 2006 to 2019, using the fixed-effect regression model and mediating-effect regression model, this study empirically tests the impact of industrial intelligence on sustainable employment in China. The following conclusions are drawn: (1) Industrial intelligence has a significant positive impact on the overall scale of employment. Industrial intelligence has promoted the optimization and upgrading of employment skill structure and industrial structure. Industrial intelligence will reduce the employment proportion of low-skilled labor and increase the employment proportion of medium-skilled labor and high-skilled labor. Industrial intelligence significantly reduces the employment share of the manufacturing sector and increases the employment share of the service sector. (2) Industrial intelligence reduces employment levels through capital deepening effects. Industrial intelligence has significantly improved regional labor productivity and significantly improved employment levels through productivity effects. (3) The results of regional heterogeneity show that industrial intelligence has promoted the improvement of employment level and the upgrading of employment structure in the eastern region but has not had a significant positive impact on other regions.

A systematic literature review on social innovation for sustainable development in China

A systematic literature review on social innovation for sustainable development in China

Towards Achieving Sustainable Development in China: What Role Does Digital Technologies and Green Technologies Play?

There is always discussion about how to use digitalization to promote sustainable development. While one side vehemently defends using ICT to direct and carry out sustainable development, another strongly struggles to highlight its many drawbacks. Similarly, green technologies are also under the limelight for promoting sustainable development since these technologies combine economic growth and environmental conservation. The purpose of the study is to empirically estimate the effect of digital technologies and green technologies in sustainable development in China. Time series data spanning over 1995-2021 period is analyzed using recently introduced Quantile Autoregressive Distributed Lag Model (QARDL). The findings of the study suggest that there exists positive association between digital technologies and sustainable development over (0.05-0.80) quantiles. The significant and positive association exists between green technologies and sustainable development at all quantiles (0.05-0.95). The study also finds positive and negative association of economic growth and urbanization respectively with sustainable development. The study results emphasize the limitations of conclusion of past studies and provide significant contributions by confirming that the associations among variables is quantile-dependent. The Wald test results also disprove the parameter constancy for the Chinese economy and the findings of Quantile Granger Causality prove the presence of bidirectional causal association between digital technologies, green technologies and sustainable development. The study recommends Chinese policy makers and government to adopt relevant policies to promote sustainable development by effectively utilizing digital and green technologies.

Study on Sustainable Development Model of Community Renewal based on the Green Development Model

The community is an important part of the city. With the city's development, it is faced with the problem of renewal and reconstruction of the old community. It is necessary to explore the sustainable development of the community. In this paper, evaluation and analysis of the coordinated development of the ecological environment and economy of the community are studied. The indicators were selected appropriately to reflect the SE and EE development status comprehensively. The results show that Nanjing, Wuhan and Xi'an take the top spot in terms of CCD, meaning it has the most harmonious relationship between the social-economic subsystem and eco-environment subsystem; Xi'an has the highest SE value, while Lianyungang has the lowest evaluation result. Nanjing has the highest EE value, indicating it has the best eco-environment development, while Suqian has the worst. This study provides a scientific basis and effective measures for sustainable development in China.

Assessing the drivers and solutions of green innovation influencing the adoption of renewable energy technologies

The degradation of the environment in China is accelerating along with economic expansion. Adoption of renewable energy technologies (RETs) is crucial for reducing the adverse impacts of economic growth on the environment and fostering sustainable development. This study attempts to identify the green innovation drivers and sub-drivers that affect the adoption of RETs in China and provide solutions for boosting their implementation. The study prioritized the drivers, sub-drivers, and strategies of green innovation by combining the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. In the study, the triple bottom line (TBL) approach has been used to determine the economic, societal, and environmental driving forces. The study also suggests strategies for encouraging the use of RETs. The results of the AHP method revealed that economics is the most crucial driver, with a weight of 0.376, followed by environmental (0.332), and social (0.291) drivers. The findings of the SAW method indicated that government green innovation initiatives, consumer initiatives, and industry initiatives are the most significant strategies for deploying RETs in China. This study has important theoretical and practical ramifications for encouraging China to adopt RETs. The suggested approaches can help researchers, business professionals, and policymakers promote sustainable development in China.

Towards a sustainable construction: A newly proposed Tapio-global meta-frontier DEA framework for decoupling China's construction economy from its carbon emissions

Uncovering the decoupling degree and its influencing factors is an important work to look for the carbon abatement and sustainable development in China's construction industry (CCI). To arrive at a solution, we proposed a research framework by taking into account the Tapio decoupling model and biased directional distance function based on global meta-frontier DEA, so as to reveal out the motive force and resistance that can help decouple construction industry economy from its carbon emissions. Discussion results indicate that during the sample period: (1) China's construction economy and carbon emissions exhibited weak decoupling. The advances of energy/production technology were two dominant factors in helping construction economy decoupled from carbon emissions, while construction economic activity exerted the largest effects in impeding decoupling process. (2) The decoupling degree and driving effects differed significantly in three regions. Regional energy/production technology disparity narrowed in central and western region, and further contributed to their decoupling. Energy-biased and output-biased scale change exerted impeding effects on the decoupling in eastern and central region, while exerted diametrically opposite effects on western region's decoupling. Besides, the change of energy/production resource allocation efficiency in eastern and western region acted motivating effects in their decoupling, while formed restriction in central region's decoupling.

Exploring Spatial-Temporal Coupling and Its Driving Factors of Green and Low-Carbon Urban Land Use Efficiency and High-Quality Economic Development in China

Green and low-carbon use of urban land (GLUUL) and high-quality economic development (HED) are two closely linked and mutually reinforcing systems, and their coordinated development is of great theoretical and practical significance to the realization of green and sustainable urban development. Based on theoretical analysis, this paper used data from 2005 to 2020 to measure GLUUL efficiency and HED level and their coupling coordination degree (CCD) successively of 282 cities in China, and then analyzed in-depth the main factors affecting CCD and its spatial–temporal heterogeneity using the GTWR model. This study found that (1) GLUUL efficiency and HED levels are increasing with different trends, and the development is unbalanced. High-value cities in the two systems show a staggered distribution pattern. (2) The CCD of the two was dominated by primary and intermediate coordination types, and the overall became increasingly coordinated, with the “intermediate coordination—advanced development” type having the highest representation. (3) There is a gradual convergence of CCD spatial differences, showing an overall spatial distribution pattern that is “high in the northwest and southeast, low in the central area”. (4) The influence degree and direction of different factors on CCD are distinguishing. The positive impact of industrial structure upgrading (Isu) is obviously greater than other factors, which has the strongest effect on the industrial corridor along the Yangtze River and the Beijing–Tianjin–Hebei region. The findings can offer insightful recommendations for promoting sustainable development in China and similar developing countries and regions.

How Does the Digital Transformation Affect the Carbon Emissions of Manufacturing Enterprises in China? The Perspective of Green Technology Innovation

With the continuous intensification of competition among major countries and the resurgence of anti-globalization trends, countries around the world have strengthened their strategic deployment for digital transformation in the manufacturing industry. Digital development enables enterprises to transform and upgrade, and the digital transformation and green transformation of enterprises have a historic intersection. This study uses the panel data of 1900 A-share listed companies to explain the theoretical mechanism of digital transformation to promote carbon emission reduction from the perspective of green technology progress. In addition, the threshold model is used to analyze the nonlinear relationship. The results show that the digital transformation has significantly reduced the carbon emission level of Chinese manufacturing enterprises, and the conclusion is still true after the instrumental variable estimation and robustness test. Heterogeneity analysis found that the carbon emission reduction in state-owned enterprises is more obvious. Green technology innovation capability shows the technology dividend effect, which is an important way for digital transformation to promote carbon emission reduction. This study expands the research on the sustainable development of China’s manufacturing industry and provides guidance for policy makers and business decision makers.

The impact of patentees assessment based on the heterogeneous

As a vital factor in technological innovation, patentee plays a significant role in the process of scientific and technological innovation, researching patentee has attracted the attention of experts and scholars. Previously, scholars have mainly quantified patent indicators or constructed homogeneous information networks to analyze patentees, but these methods cannot objectively measure the impact of patentees. Therefore, this study proposes a novel approach to assessing patentee impact based on a heterogeneous information network. The proposed method distinguishes the weight of different types of nodes using a weighted mechanism and extracts three types of fine-grained characteristics of network nodes. This approach results in the construction of a heterogeneous patent innovation network and the development of a new patentee impact assessment algorithm called CWAPN. Using Chinese green patents in the field of energy conservation and environmental protection as an example, experimental results show that the CWAPN algorithm can effectively assess the impact of patentees. Thereby identifying patentees who have made outstanding contributions to sustainable development in China.

Carbon reduction effects of digital financial inclusion: Evidence from the county-scale in China

All countries in the world are faced with varying degrees of environmental pollution problems and carbon reduction responsibilities. In the case of China, digital transformation emerges as a new growth catalyst for the future economy and a pivotal avenue for optimizing resource allocation, enhancing energy structure, and reducing energy dependence for development. Therefore, this study delves into the carbon reduction effect of digital financial inclusion and explores its pathways. It focuses initially on the counties of the Yangtze River Delta in China, utilizing nighttime light data to estimate carbon emissions and scrutinizing the spatial and temporal distribution characteristics of emissions. Empirical results reveal that digital financial inclusion positively influences carbon emission reduction, with notable spatial and temporal variations in the emission reduction effect. Furthermore, the study uncovers that digital financial inclusion contributes to reducing carbon emissions through the optimization of industrial structure and the promotion of technological innovation. These research findings offer a scientific foundation for guiding the government in developing digital finance to attain local carbon emission reduction targets. Concurrently, local governments should leverage the role of digital financial inclusion in optimizing industries and driving technological innovation to propel the low-carbon, sustainable development of China.

Spatial effects of green innovation and carbon emission reduction in China: Mediating role of infrastructure and informatization

Green innovation (GI) plays a vital role in mitigating urban carbon dioxide (CO2) emissions and fostering sustainable development in China. This study examines the impact of GI on CO2 emissions, underscoring its significance for environmental sustainability. The analysis of data from 253 Chinese cities from 2010 to 2020 shows a transition in GI from a “multipoint” sporadic pattern to a “clustered” aggregation, indicative of significant polarization. Concurrently, a converging trend in the CO2 emissions kernel density curve reflects the initial success of carbon reduction initiatives, demonstrating a notable “high in the north and low in the south” spatial disparity. Critically, the findings reveal that GI induces significant spatial spillover effects, reducing CO2 emissions not only locally but also in adjacent areas. The mediation analysis reveals that the level of infrastructure development acts as a masking factor, suggesting that despite infrastructure expansion potentially escalating CO2 emissions, alignment with GI principles steers its environmental impact towards a positive direction. Additionally, informatization's modest mediating role suggests that its enhancement, driven by GI, further mitigates CO2 emissions through improved resource efficiency. These insights highlight the importance of integrating GI into urban policy frameworks, emphasizing its role in environmental management and CO2 emission reduction.

Spatiotemporal Dynamics and Influencing Factors of Vegetation Net Primary Productivity in the Yangtze River Delta Region, China

Vegetation Net Primary Productivity (NPP) plays a crucial role in terrestrial carbon sinks and the global carbon cycle. Investigating the spatiotemporal dynamics and influencing factors in the Yangtze River Delta (YRD) region can furnish a solid scientific foundation for green, low-carbon, and sustainable development in China, as well as a reference for other rapidly urbanizing regions. This study focuses on the YRD region as an illustration and utilizes the Carnegie–Ames–Stanford Approach (CASA model) to quantify NPP in this region from 2000 to 2018. Investigation into the spatiotemporal dynamics and influencing factors was conducted using Theil–Sen median trend analysis and scenario analysis. The results indicate that the NPP in the YRD region from 2000 to 2018 exhibited pronounced spatial differentiation characteristics, typically exhibiting a spatial distribution pattern of being high in the south and low in the north, high in the west and low in the east. Additionally, the expansion of built-up areas and the reduction in cultivated land have the potential to reduce NPP in the YRD region. Moreover, the influence of land-use and land-cover change (LULCC) is anticipated to be relatively limited compared to that of climate change. Furthermore, changes in precipitation were found to be positively correlated with changes in NPP, with the effect being relatively more pronounced. The correlation between temperature and NPP demonstrated spatial differentiation, with a mainly positive correlation in the central and southern parts of the YRD and a mainly negative correlation in the northern part. Changes in solar radiation had a negative correlation with changes in NPP. Based on these results, it is recommended that local governments strictly enforce urban development boundaries and manage the disorderly expansion of built-up areas, enhance the regional irrigation infrastructure, and address air pollution, so as to ensure the necessary conditions for the growth of vegetation, reduce greenhouse gas emissions, and control regional temperature rises. This study can provide stronger evidence for revealing the influencing mechanisms of NPP through the control of impact conditions and the exclusion of confounding factors via scenario analysis. The policy implications can offer insights into NPP enhancement and environmental management for the YRD and other rapidly urbanizing regions.

Exploring the dynamic capability of green technology innovation for achieving sustainable development: an empirical insight from China

ABSTRACT During the past several decades, the world has been putting strong emphasis on the role of environmental technology innovation which can reduce environmental degradation and promote sustainable development. This research sheds light on the impact of green technology or eco innovation on sustainable development for China. Additionally, the effects of other variables such as governance, information and communication technology, trade uncertainty and urban population are also analysed. The study employs Fully modified ordinary least squares (FMOLS) and Canonical Cointegration Regression (CCR) as the long run tests and Breitung and Candelon Spectral Granger-Causality as the time varying granger causality test. The results suggest that green innovation promotes sustainable development in the long run. The roles of governance quality and urbanization are also found to be positive while the role of ICT is found to be insignificant yet negative. The role of trade uncertainty is positive yet insignificant. Based on these results, the study calls for the private and public organizations and industries to invest in eco innovation. Different incentives from the government can also encourage private industries to invest in green innovation technologies.

Effectiveness evaluation of China's water resource tax reform pilot and path optimization from the perspective of policy field.

The water resource tax reform played an important role in promoting sustainable development in China. Subsequent to the seven-year reform, the effectiveness evaluation of the policy in each pilot area and the exploration of the optimization path directly affected the promotion of water resource tax policy and the improvement of water use efficiency. Therefore, the theoretical framework of the water resource tax policy field was constructed to examine the mechanism of the three subsystems of policy scenario, policy orientation, and policy effect; fuzzy-set qualitative comparative analysis (fsQCA) was then used to evaluate and quantitatively compare the policy implementation effect and policy path in each pilot area, with emphasis put on three policy orientations, i.e., the decision and decomposition effect of policy goals, the selection and im plementation effect of policy tools, and the policy supervision and security effect. As shown by the research results: ① the water resource tax reform had effectively improved the efficiency of water resource utilization in the pilot areas; ② three pilot models of water resource tax policy had been extracted, namely the policy goal and tool-driven model centering on a single dimension of the policy field, the implementation-supervision dual drive model emphasizing the supervision and security effect of the policy, and the three-dimensional policy orientation linkage model that focused on the synergistic effect of the policy field; ③ strong heterogeneity existed in water resource tax policy implementation paths and effects in each pilot area. Accordingly, regional heterogeneity could be considered in the process of reform to construct institutionalized, precise, and differentiated reform implementation methods from the perspective of the policy field.

Mineral resources and intellectual capital: Asymmetrical impacts and sustainable development outcomes

The existence and use of natural resources, and the impact that they have on sustainable development is a topic that has received significant attention, with a particular focus on the “resource curse hypothesis.” However, there is limited research that explores the combined role of natural resources and human capital, specifically when it comes to the realm of endogenous sustainable development. This study, therefore, takes into account this research gap, and investigates the interrelationship between natural resources, human capital, and sustainable development, specifically in the context of China. By conducting an advanced econometric analysis, we have examined the influence of oil rents, forest rents, and mineral rents on China's economic growth from the years spanning from 1992 to 2022, while considering the control variables such as foreign direct investment (FDI) and research and development (R&D) expenditures. Our findings have revealed an asymmetric impact of mineral rents and oil rents on economic growth, while forest rents have shown a positive and considerable influence as well. This essentially indicates the presence of beneficial effects from forest resources. Moreover, we have also observed a positive association between human capital and economic expansion. in addition to this, expenditures such as those pertaining to FDI and R&D are shown to stimulate economic growth in China. Furthermore, this study also finds bi-directional causality between oil rents, FDI, and human capital, while forest rents, mineral rents, and R&D expenditures show an uni-directional causal relationship with economic growth in China. Our study contributes towards the understanding of the complex dynamics that exist between natural resources, human capital, and sustainable development in China. These findings have policy implications for sustainable development through resource management, human capital development, and technological innovation.

Impact of green finance on green energy efficiency: A pathway to sustainable development in China

Green energy efficiency (GEE) is pivotal for sustainable economic growth, particularly in the context of China's ambitious environmental goals. This study assesses the impact of Green Financial Reform and Innovation Pilot Zones on GEE from 2005 to 2020, using the SBM-GML method and a Double Difference Model (DID). Findings indicate a significant improvement in GEE within these zones, driven by advancements in green innovation and environmental regulation. At the micro level, firms in pilot zones show enhanced Environmental, Social, and Governance (ESG) performance and experience fewer financial constraints. The effectiveness of green finance policies is more pronounced in resource-rich and financially efficient regions, suggesting that these initiatives can significantly contribute to sustainable development. Based on empirical evidence, we recommend expanding pilot zones, promoting green technological innovation, and developing tailored green finance policies to address regional ecological sensitivities.

Analyzing the relationship between natural resource management, environmental protection, and agricultural economics for sustainable development in China

Sustainable development is crucial for achieving a balance between economic growth, social well-being, and environmental preservation. This study aims to evaluate the dimensions of natural resource management, environmental protection, and agricultural economics for promoting sustainable development in China. In this regard, the fuzzy Analytical Hierarchy Process (AHP) and fuzzy VIekriterijumsko KOmpromisno Rangiranje (VIKOR) approaches are used for the analysis. The hybrid approach combines the benefits of fuzzy logic and multi-criteria decision-making (MCDM) techniques, allowing for the consideration of uncertainties and imprecise data in decision-making. The study incorporates six criteria, twenty-four sub-criteria, and five strategies in a comprehensive evaluation framework. According to the results obtained using the fuzzy AHP method, it is evident that the primary criteria influencing sustainable development in China are economic viability, policy and governance, and environmental impact. Within these criteria, specific sub-criteria such as cost-effectiveness, policy alignment, air quality, and social inclusion emerge as key areas of focus. Furthermore, the results of the fuzzy VIKOR method show that investing in research and innovation, strengthening environmental regulations, and strengthening climate change adaptation are the most suited strategies to guide sustainable development efforts. To ensure sustainable development in China, policymakers need to give importance to implementing strategies stressing research and innovation, strengthened environmental regulations, and enhanced climate change adaptation.

Risk management of green supply chains for agricultural products based on social network evaluation framework

AbstractThe green supply chain of agricultural products (GSCAP) is a key link for rural revitalization and sustainable development in China. However, it faces various risks from internal and external environments that threaten its performance and stability. This paper proposes a novel framework and system for identifying and evaluating the main risks in the GSCAP from the perspective of agricultural enterprises. The framework combines social network analysis (SNA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method. SNA is used to analyze the correlations and influences among different types of risks, while the improved TOPSIS method is used to rank the risks of different GSCAPs and identify the key risks in each supply chain. The framework and system are verified by a case study of CDYBIT, a leading platform of food safety big data service in China. The results show that the supermarket supply chain has the highest risk, followed by the group catering supply chain, and the five‐star hotel supply chain has the lowest risk. The main risk factors for each supply chain are also discussed, and some suggestions for risk management are provided. This paper contributes to the literature by providing a comprehensive and systematic risk assessment framework and system for the GSCAP, which can help agricultural enterprises improve their risk awareness and response capabilities.

Study on urban land use in Henan Province under the background of high-quality development

With the rapid development of the economy and continuous urban expansion, the sharp increase in land demand under the existing land scale and the limited supply of land resources have led to a serious intensification of contradictions. Improving land use efficiency, especially construction land use efficiency, has become an inevitable direction for future development. This is also a challenge faced by the comprehensive and sustainable development of China’s urban economy and society. Among other benefits, evaluating the efficiency of urban construction land use can maximize the distribution of land resources, improve the overall advantages of construction land, and encourage land management. This paper establishes and illustrates the evaluation based on three aspects: economic benefits, social benefits, and ecological benefits, to establish an index system for the evaluation of urban construction land use efficiency in Henan Province. In this paper, the coordination trend and efficiency of land use in urban construction in Henan Province are analyzed, discussed, and evaluated.