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To gain a deeper understanding of the intrinsic dynamic relationship between energy consumption and economic growth in China. This study employs panel cointegration and causality models, utilizing the SYS-GMM technique to assess the factors influencing economic growth in China's green finance sector from 2002 to 2022. The research explores the interactions among multiple variables related to the Chinese economic context, including economic growth, carbon dioxide emissions, total natural resource rents, energy consumption, and environmental impact. While considering key factors that may cause structural disturbances in the time series analysis. The findings indicate the existence of long-term cointegration relationships among these variables, with positive correlations between economic growth and total natural resource rents, energy consumption, energy quantity, and ecological footprint. Results also show a bidirectional causal relationship between carbon dioxide emissions and energy consumption and a unidirectional correlation between energy consumption and GDP growth. Additionally, energy intensity (EI) improvements supported by green finance are linked to a significant reduction in CO2 emissions, with a coefficient of -1.933 (p < 0.05), underscoring the role of technological innovation. Further evaluations suggest that investments in renewable energy can promote economic growth, create job opportunities, and reduce greenhouse gas emissions. Energy-saving measures and green finance-supported technological innovations play crucial roles in improving energy intensity and reducing CO2 emissions. The study also underscores the importance of economic diversification to reduce dependence on natural resources and enhance economic stability. Future research should further explore the economic feasibility and environmental benefits of emerging technologies such as Carbon Capture and Storage (CCS), providing deeper insights into sustainable energy practices. [ABSTRACT FROM AUTHOR]

Challenges brought on by the rapid rate of economic growth include the depletion of natural resources (NAR) and environmental deterioration. Due to its abundant mineral resources and rapid economic growth, China has gained attention from both the developing and developed worlds. Using data from 1970/Q1 to 2020/Q4, this paper evaluates the time‐frequency nexus between CO2 emissions and natural resources, coal consumption, and economic growth. The study employs the Wavelet Local Multiple Correlation (WLMC) to explore this nexus. This approach enhances the capacity to comprehend the fundamental relationships between these indicators at different frequencies. To the best of the investigator' knowledge, this is the first empirical investigation to explore the time‐frequency nexus between natural resource, economic growth, coal use, and CO2 emissions in China, thus filling a gap in the literature. Furthermore, wavelet coherence is employed as a robustness check. The WLMC bivariate cases revealed that economic growth, coal consumption, and natural resources drive CO2 emissions positively at all frequencies, that is, in the short and long‐term, thus leading to environmental degradation. Furthermore, the wavelet coherence results support the outcomes of the WLMC bivariate cases. This study offers significant implications for policy and insights that are supported by the findings. [ABSTRACT FROM AUTHOR]

In this study, the relationship between electricity and growth of the economy is investigated by applying the newly-developed bootstrap autoregressive-distributed lag test with a Fourier function to examine both the causality and cointegration for China, India, and the United States (US). While it is not possible to detect a long-term cointegration relation among the economy's electricity and growth, the study findings demonstrate the contingency of the causality. The ensemble method in machine learning performs better than conventional methods as electricity is an independent indicator for forecast economics. Concerning the US, previous electricity consumption has a positive impact on the current nature of economic growth. In contrast, the consumption of electricity is negatively affected by the development of the economy. However, for China and India, positive and negative feedback can be observed, respectively. Due to the increased awareness of the environment's adverse effects, China should promote technologies that conserve energy and boost energy efficiency to achieve sustainable development in both environmental and economic terms. In India's context, broadening access to electricity has significance for residents in rural areas and enhances economic growth. It is recommended that policy-makers promote innovative technologies in the US, as the abundant natural and human resources can make valuable contributions to the society and development of the economy. [ABSTRACT FROM AUTHOR]

In tandem with global initiatives to 'go green', China is undertaking a series of steps to achieve green economic growth. To investigate the dynamic nexus of green growth, green trade, and green energy (3 G) in China, an index is developed in this study to assess the level of provincial green growth by employing five types of indicators – economic growth, environmental pollution loss, carbon emissions loss, natural resource loss, and environmental and natural resource benefits. Then, this paper uses the SYS-GMM method to explore the influences of green trade and green energy on green growth by using data compiled from 30 provinces in China over the period 2007–2016. Furthermore, we check the potential heterogeneity, asymmetry, and internal mediating mechanism of the 3 G nexus. The main findings are highlighted as follows: (1) Green trade and green energy can accelerate China's green growth; (2) enhancing medium- and high-technology green trade can contribute to improving local green growth; (3) this impact is heterogeneous in regions with different trade levels, and asymmetric at various quantiles for the full panel; (4) the positive investment effect, labour effect, and technical effect are effective mediators of the nexus between green trade and green growth. [ABSTRACT FROM AUTHOR]

This study investigates whether natural gas exploitation is a blessing or a curse for provincial and prefecture economic growth. This study employs regression models and synthetic control methods (SCM) to investigate the research question based on hybrid panel and time-series data from 1993 to 2015 from 14 prefecture cities in Sichuan province in China. Based on the provincial data of Sichuan, the regression results show that natural gas exploitation is a curse for the provincial economic development of Sichuan because of the negative associations between gas resources and economic performance, for which the increasing rent-seeking in the province may partially account. However, the SCM results find that the local economy of Dazhou city may benefit from its sufficient natural gas resources in the short run, but it is not sustainable. Meanwhile, the gas resource has no significant contributions to the regional economic growth of Sichuan overall, which is partially attributed to the fact that resource exploitation has no significant spillover effects on some sustainable contributors, such as education, innovation, and others. The practical implication for the economic governors is that the resource-consumed economic route is not sustainable, although it may drive local economic growth in the short run. [ABSTRACT FROM AUTHOR]

Regional efficiency refers to the ratio of the effective total output and the total input of regional factor resources under certain production technology conditions. At present, China's traditional economic growth mode is based on the mode of sacrificing the environment for economic growth. At the same time, the influence and restriction of natural resources and natural environment on human social and economic development are more obvious than ever before. The contradiction between social and economic development, resources, and environmental preservation has become increasingly prominent. Problems such as resource shortage, environmental pollution, and ecological imbalance have exposed the disadvantages of the traditional development model, and so human beings have begun to seek new high-quality development models. Therefore, the research on regional efficiency in this paper is based on the idea of coordinated development of resources, environment and social economy, and multi-objective optimization algorithm, which constructs the evaluation index system of regional efficiency, comprehensively measures the regional efficiency, and inspects the development status. Evaluating the regional efficiency is conducive to analyzing the problems existing in regional development, saving resources, and promoting the sustainable development of the region. [ABSTRACT FROM AUTHOR]

In modern era, human resource is a crucial factor for economic development. Several studies reveal that rapid economic growth of developed countries has been linked with investment in human capital. Efficient and proper management of the workforce is an important factor in development of a country. The importance of human capital formation is now fully recognized because healthy and skilled people, along with physical capital, help in capital formation and raising economic growth. Therefore, large scale investment in human capital is required for the full and optimum use of natural resources. Improvement in the health of masses increases their productive capacity and leads to quantitative improvement in human capital. This paper examines the role of health facilities in human capital formation as well as in economic development. The current study analyzes the trends of current health expenditure as percent of GDP in India and China. An effort has been made to compare the impact of health expenditure on value added per worker in agriculture and its allied sectors in India and China by employing multiple regression model using data from 2000 to 2017. Findings reveal that independent variable (current health expenditure) explains the variation in dependent variable (value added per worker) to an extent of 37.9% and 56.9% in India and China, respectively. [ABSTRACT FROM AUTHOR]

China has been undergoing rapid economic growth since its reform and opening-up at the expense of resource depletion and environmental pollution. We estimated the monetary costs of resource depletion and environmental pollution in China over the past 14 recent years by establishing integrated mathematical models with System for Integrated Environmental and Economic Accounting (SEEA) framework. Results showed four points. Firstly, the total temporal costs of natural resource depletion and environmental pollution witnessed an inverted 'U' shape pattern with the turning point in 2012. The relationship between cost of mineral resource depletion, as well as water pollution cost, and per capita GDP were found to follow the Inverted-U shape environmental Kuznets curve (EKC). Secondly, the average annual share of natural resources of the total costs was more than 80%, indicating that the rapid economic growth of China is highly dependent on resource depletion. Thirdly, Although China still has the highest worldwide in its total energy consumption and CO 2 emission, China's struggles with adjustment of industrial & energy consumption structure, and pollution controls have resulted in a general temporal decline of the share of resource and environmental costs to China's GDP. The green GDP of China is growing at an average rate of 14.76%. Finally, although in the process of manufactured goods for the whole world, China has reduced environmental pollution and left it at home as much as possible. Driving forces behind the progress in green GDP of China were analyzed by using Differences-in-Differences method and Spearman rank correlation analysis. [Display omitted] • An integrated model was proposed to calculate China's costs of resources and environment. • Total costs of resource depletion and environment witnessed an inverted 'U' shape. • Driving forces behind decreasing trend of green GDP in China were analyzed. [ABSTRACT FROM AUTHOR]