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The impact of digital industrialization on regional pollution control and carbon reduction in China is an area that remains largely unexplored despite being a new driving force in promoting high-quality economic development. This study constructs a combined system synergy model to measure the synergistic governance effect of regional pollution and carbon reduction in China from 2011 to 2020 and then estimates the direct impact and spatial spillover effect using a spatial dual-weight model. Our findings indicate that digital industrialization has a greater impact on regional pollution reduction and carbon reduction as geographical distance decreases, with the spillover effect with close geographical relationships being higher than that of adjacent. Furthermore, the heterogeneity analysis reveals that the added value of digital technology and services has a significantly positive effect, while the spatial spillover effect of the added value of digital infrastructure is significantly negative. Finally, our mechanism judgements prove digital industrialization can impact the level of regional co-governance of pollution and carbon reduction through source prevention, process control, and end-treatment. Our study provides a factual basis for further promoting China's environmental pollution control and carbon reduction behavior and offers a method to use different spatial weights in depth. [ABSTRACT FROM AUTHOR]

The BRICS countries—Brazil, Russia, India, China, and South Africa—are committed to achieving United Nations Sustainable Development Goal 13, which focuses on mitigating climate change. To attain this goal, it is crucial to emphasize the significance of ICT, renewable energy sources, industrialization, and institutional quality. This study contributes to the literature by examining the potential role of these factors in environmental sustainability in the BRICS economies from 2000 to 2021, utilizing cross-sectional augmented autoregressive distributed lag (CS-ARDL) estimation and other novel econometric techniques. Accordingly, the study suggests that BRICS governments and policymakers prioritize the use of ICT in the industrial and institutional sectors to achieve faster environmental sustainability in the short-run, as per the CS-ARDL results. However, the study advises caution in the long-term as the interaction between ICT and renewable energy sources, industrialization, and institutional quality may not favour environmental quality. Although the renewable energy sources interaction with ICT may not yield immediate progress, strong measures need to be taken to ensure that short-term gains are not nullified. In conclusion, the study highlights the potential of ICT, renewable energy sources, industrialization, and institutional quality in achieving environmental sustainability in the BRICS countries, while recommending cautious measures in the long run to safeguard the progress made. [ABSTRACT FROM AUTHOR]

Encouraging the recycling of plastic packaging materials in express delivery is a necessary step toward environmentally friendly industrial development. In this study, we present a framework for analyzing the flow of materials in express plastic packaging, from production and manufacturing to consumption and recycling. In examining the use of recycled materials in post-consumer express plastic packaging and the destination of consumer packaging waste in 2020 and 2021, we found that 44.4% (1613.6 Gg) of the studied express plastic packaging was incinerated. Additionally, approximately 1296.6 Gg of express plastic packaging flowed into rural areas. Our calculations showed that the ΔRSE in 2020 was 15.1%, and on the condition that 25% separated collection with 80% recycling, ΔRSE would be − 0.5%. Results verified that separated collection is an important step in the recycling strategy for packaging materials. Survey data from universities in Beijing indicate that currently, 26% of college students are participating in the separate collection of packaging. [ABSTRACT FROM AUTHOR]

Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties. [ABSTRACT FROM AUTHOR]

The investigation of the implementation effect of the low-carbon city pilot (LCCP) policy in promoting industrial low-carbon development is important for economic growth and carbon reduction in China. This paper takes the industry sector as an independent investigation object, and aims to examine the effectiveness of the policy in improving industrial low-carbon development. The industrial total factor carbon emission efficiency index (ITFCEE) is constructed to measure the level of industrial low-carbon development by the global frontier directional distance function. From the perspective of the multiple participants of the policy, we analyze the mechanisms of the policy, and construct the time-varying difference-in-difference model to test how LCCP policy impacts the ITFCEE. We conclude that (1) LCCP policy improves significantly the ITFCEE. (2) Heterogeneity analysis results show that the positive influence of LCCP policy is significant in cities with high industrial carbon emission intensity, high industrial structure upgrading, and high government expenditure. (3) The result of the mechanism analysis is that LCCP policy can benefit the ITFCEE by promoting enterprise green technology innovation, public environment concern, government technology support, and administrative regulation. [ABSTRACT FROM AUTHOR]

Rio Grande is a city located on a narrow industrialized and urbanized Brazilian peninsula, characterized by wetlands. Due to population growth, numerous urban backfilled regions were built to expand the territorial area of the city. Currently, more than 60% of the central area of the city comes from the grounding of wetlands. The material used for the expansion of the territory had a history of contamination from metals from the tannery and textile industries (mainly Hg) and urban solid waste. In addition to past sources, the city has an active industrial complex with fertilizer, petrochemical, and grain industries. This study evaluated the risks to human health caused by metals (Hg, Fe, Ni, Cr, Cu, Pb, and Zn) in original soils and backfills, considering the oral, inhalation, and dermal routes of exposure for children and adults using the tool human health risk assessment (HHRA) proposed methodology by USEPA. A total of 63.81% of the original soil samples and 57.14% of the backfill soil samples showed a non-carcinogenic risk (HInc>1) for at least one evaluated metal. Still, approximately 10% of the samples presented carcinogenic risk when the Cr was considered in the hexavalent form. The dermal (Hg, Ni, and Cr) and oral (Fe, Cu, and Zn) exposure routes had the greatest contribution to the total risk. The non-carcinogenic risk for Hg, Cr(VI), and Pb was heterogeneously distributed between the original soils and backfills and associated with the proximity to some pollution sources. Given the complexity of historical occupation in the municipality and the increasing industrialization, both the original areas and the backfills should be included in the risk management strategy to minimize risks. [ABSTRACT FROM AUTHOR]

Microalgae can use carbon sources in sludge extract prepared from sludge. Moreover, the high concentration of CO2 and the large number of carbon sources in the liquid phase will promote microalgae growth and metabolism. In this experiment, Tetradesmus obliquus was cultivated with sludge extract at 30% CO2. Algae liquid (the name used to describe the fertilizer made in this research) was further prepared as lettuce fertilizer. The effect of different times of microalgae culture (10, 15, 20, 25, and 30 days) on the fertilizer efficiency of the algae liquid was evaluated by lettuce hydroponic experiments. The findings indicate that lettuce cultivated in algae liquid collected on the 15th and 30th days exhibited superior performance in terms of growth, antioxidant capacity, and nutritional quality. We analyzed the experimental results in the context of microalgae metabolic mechanisms, aiming to contribute experience and data essential for the development of industrial microalgae fertilizer production. [ABSTRACT FROM AUTHOR]

This study is an attempt to comparatively analyze the impact of renewable energy sources on air quality represented by particulate matter 2.5 concentrations utilizing panel data of 60 countries which are divided into two sub-panels industrialized economies and emerging industrial economies over the period 2010–2019. The study adopts both demand- and supply-side approaches and hence renewable sources are handled in two different structures, i.e., renewable energy consumption and production. Empirical results from both demand- and supply-side regressions strongly confirm the positive impact of renewable sources on air quality in all country groups, meaning that higher renewable energy production and consumption bring about improvement in air quality. In addition, this positive impact of renewables on air quality turned out to be higher in emerging industrial economies than that in industrialized ones. To be more precise, as all control variables are considered, a 10% increase in the production of renewable energy sources brings about a 0.66% improvement in air quality in industrialized economies while its impact is a value of 1.33% in emerging industrial economies. On the other hand, a 10% increase in consumption of renewable energy sources leads to a 0.62% improvement in air quality in industrialized economies and a 1.97% improvement in emerging industrial economies. As for control variables, industrialization gives rise to an increase in air pollution in all country groups, whereas economic growth and trade openness function as favorable factors for air quality. Although population density improves air quality in industrialized economies, it is found as one of the main pollutant factors in emerging industrial economies. Overall results proved that renewable sources improve air quality by reducing particulate matter 2.5 concentrations. Therefore, these countries, especially emerging industrial economies, should replace primitive energy sources like fossil fuels with renewables to bring down environmental degradation up to a reasonable level and increasingly continue to invest in renewable energy domain to reach their environmental sustainability targets. The study also provides some additional policy implications. [ABSTRACT FROM AUTHOR]

This paper aims to detail the relationships between urbanization, industrialization, the innovation ability of cities and local air quality in 284 cities in China using annual data. For the empirical outputs, the panel quantile regression analysis, which considers the heterogeneous nature of the data set, is employed. Initial findings indicate that (i) urbanization and industrialization negatively affect local air quality. (ii) Innovation capability of cities has a direct and improving impact on local air quality. Then, the paper estimates the moderating role of cities' ability to innovate in the polluting effect of urbanization and industrialization on local air quality. Remarkably, empirical evidence indicates that (iii) the innovation ability of cities also moderates the polluting impact of urbanization and industrialization on local air pollution. Based on the findings, the paper confirms the importance of both direct and moderator effects of the innovative environment in cities in tackling air pollution. [ABSTRACT FROM AUTHOR]

It is an important way for mining the characteristics of regional carbon emission efficiency and exploring regional similarity to learn from intergovernmental learning mechanisms and regional industrial low-carbon development experiences. This study proposes a regional learning mechanism of industrial carbon emission efficiency (ICEE) prediction and regional similarity analysis to explore strategies for carbon emission reduction. We first calculated the industrial carbon emission efficiency of 30 provinces in China from 2000 to 2021 using the super-SBM model. Secondly, the spatiotemporal characteristics of industrial carbon emission efficiency were explored through the space-time cube model, time series clustering method, and local outlier analysis. Finally, the screening of regions with low efficiency levels and the search for learning objects were realized by forest regression prediction and regional similarity calculation. The results of the study were as follows: (1) There were significant differences in industrial carbon emission efficiency among different provinces. (2) Based on the time series clustering results, we found that there were similar change characteristics of industrial carbon emission efficiency in different provinces. (3) The industrial carbon emission efficiency of most provinces had significant correlation in space and time, mainly in high-high clustering. (4) The industrial carbon emission efficiency of most regions will maintain a high efficiency level in the next 10 years, but the six provinces of Xinjiang, Qinghai, Gansu, Ningxia, Liaoning, and Heilongjiang will always be at a low efficiency level. It is possible to set appropriate learning targets for each region and to find lessons to be learned from the regions with high similarity by calculating the similarity between each province and the six provinces. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and industrialization, the increasingly prominent contradiction between urban industrial development and urban land supply and demand highlights the need to explore the intricate interplay between the green transformation of urban construction land (GTUCL) and industrial transformation and upgrading (ITU). This exploration carries significant practical implications for fostering the harmonious development of industry and land use and achieving sustainable urban growth. In this study, we constructed evaluation indicator systems for GTUCL and ITU, drawing on the analytical frameworks of "dominant-recessive" transformation theory and "dynamic structure benefit" theory, respectively. We employed an enhanced entropy method, an improved coupling coordination model, and a geographic detector to unveil the coupling coordination relationship, the spatiotemporal evolution patterns, and the driving mechanisms of GTUCL and ITU within the urban agglomeration of the mid-Yangtze River region. The results are as follows: (1) during the research period, the comprehensive index of ITU exhibited substantial growth, increasing from 0.159 to 0.408, reflecting a remarkable 157% surge. Simultaneously, the comprehensive index of GTUCL witnessed an increase from 0.171 to 0.285, signifying a 66.66% upswing, lagging behind that of ITU. (2) The degree of coupling coordination between ITU and GTUCL demonstrated a consistent upward trajectory. It rose from 0.402 to 0.579 by 2020, marking a significant 44.2% increase. The overall development now resides within the stage of coordinated progress, although it remains at a relatively modest level, and the pace of development has moderated somewhat. Regional disparities within the urban agglomeration consistently manifest a trend of initial reduction followed by amplification, accentuating the polarization effect of internal coupling relationships. (3) The spatial differentiation pattern centered around "Wuhan, Changsha, and Nanchang" has become increasingly pronounced. The radiation linkage model following the "core–periphery" paradigm has stabilized further. Nevertheless, the relatively loose spatial connection structure has led to a more conspicuous inadequacy in radiation impact. (4) The core driving factors have transitioned from emphasizing economic development to focusing on technological support. The composition structure has shifted from the initial stage of an "economically sensitive" driving model, which centers on economic development, to the second stage of an "ecologically sensitive" driving model, which prioritizes ecological development. [ABSTRACT FROM AUTHOR]

High population, energy consumption, industrialization, and environmental degradation are inherently linked, making the study of ecological footprints in the most populous countries crucial for understanding their environmental impact and guiding efforts to minimize ecological degradation through sustainable resource management and conservation. Therefore, this study examines the effects of disaggregated energy consumption, industrialization, and total population on the ecological footprint of the world's top 10 most populous countries namely Bangladesh, Brazil, China, India, Indonesia, Mexico, Nigeria, Pakistan, Russia, and the USA, using data for the period of 1990–2020. The research employs Kao and Pedroni techniques of cointegration to determine whether the variables are cointegrated in the long run. The long-term equilibrium association is measured utilizing panel autoregressive distributed lag/pooled mean group (ARDL/PMG), and method of moment quantile (MMQ) regression methods. Furthermore, to test for the causal relationships between the selected variables, we used the Dumitrescu and Hurlin (D-H) panel causality method. The findings of the study reveal that renewable energy consumption, as well as GDP square, have a significant negative influence on ecological footprint, implying that renewable energy and GDP square reduce ecological footprint and thus enhance environmental quality. Furthermore, non-renewable energy, industrialization, total population, and GDP have a detrimental impact on environmental quality by increasing ecological footprint. It is also found that there is a one-way causality from non-renewable energy and industrialization to ecological footprint and a bidirectional causal relationship between ecological footprint and total population, GDP, and GDP2. Important policy implications are drawn based on the findings. [ABSTRACT FROM AUTHOR]

To achieve peak carbon neutrality, it is necessary to transform energy structures, promote industrial ecology, and enhance energy efficiency. Employing provincial panel data from 2011–2020, we empirically investigate the impact of financial technology on energy efficiency and the role of industrial ecology in the development of financial technology to enhance energy efficiency. The findings reveal that financial technology can significantly enhance energy efficiency and promote it by improving industrial ecology. Furthermore, industrial ecology can act as a crucial channel for financial technology to promote energy efficiency. Specifically, an increase of one standard deviation in financial technology is associated with a 33.1% surge in energy efficiency on average. Financial technology has made a significant contribution to the improvement of energy efficiency in all regions, albeit with regional heterogeneity. Under different threshold variables, financial technology exerts a facilitating effect on the enhancement of energy efficiency; the impact of financial technology on energy efficiency remains heterogeneous. [ABSTRACT FROM AUTHOR]

Can the adoption of information technology (IT) by firms drive green innovation and stimulate sustainable development? This paper employs a quasi-natural experiment, namely the "Pilot Zone for Integration of Informatization and Industrialization" in China, to evaluate the effect of IT on green innovation. Our findings reveal that IT significantly fosters the proliferation of green invention patents but has inconsequential impact on green utility model patents. Notably, the positive effects of IT on green innovation are further amplified when firms receive scrutiny from analysts. Moreover, IT can facilitate green innovation in the source-governance category of conventional energy and not only in end-of-pipe regulatory compliance. The study generates valuable insights regarding the utilization of information technology by firms as a means to facilitate green innovation. [ABSTRACT FROM AUTHOR]

The petrochemical industry is an integral contributor to the global economy and plays a critical role in improving the lives of people worldwide. However, its development has also led to frequent accidents, and the evolution and global impact of these risks have not been adequately assessed. This study is aimed at quantitatively analyzing the current state of petrochemical risk and its impact on industrial development in 58 coastal countries (regions) worldwide by identifying petrochemical risk impact indicators and constructing threshold regression models. We showed that the global petrochemical risk has been unevenly decreasing in recent years. In some countries (regions), the petrochemical risk remains unchanged or is increasing; in particular, coastal provinces of China need to address this issue. Moreover, when industrialization and economic development levels are < 53.6 and 34,918.4 USD/person, respectively, petrochemical accidents negatively affect industrial development; however, above these thresholds, they do not impact industrial development. Coastal areas of China and some developing countries are far below these thresholds. Therefore, the risk from petrochemicals remains a critical factor affecting industrial development in countries with low industrialization levels. [ABSTRACT FROM AUTHOR]

Environmental concerns due to the release of industrial wastewater contaminated with dyes are becoming more and more intense with the increasing industrialization. Decolorization of industrial effluents has become the top priority due to the continuous demand for color-free discharge into the receiving water bodies. Different dye removal techniques have been developed, among which biodegradation by laccase enzyme is competitive. Laccase, as a green catalyst, has a high catalytic activity, generates less toxic by-products, and has been extensively researched in the field of remediation of dyes. However, laccase's significant catalytic activity could only be achieved after an effective immobilization step. Immobilization helps strengthen and stabilize the protein structure of laccase, thus enhancing its functional properties. Additionally, the reusability of immobilized laccase makes it an attractive alternative to traditional dye degradation technologies and in the realistic applications of water treatment, compared with free laccase. This review has elucidated different methods and the carriers used to immobilize laccase. Furthermore, the role of immobilized laccase in dye remediation and the prospects have been discussed. [ABSTRACT FROM AUTHOR]

Sedimentary mercury (Hg) records from remote areas are significant for revealing historical variations of regional Hg and understanding the influence of regional and global Hg emissions. In this study, sediment cores were retrieved from two subalpine lakes in Shanxi Province in North China and employed to reconstruct atmospheric Hg variations over the last two centuries. The two records show similar anthropogenic Hg fluxes and evolution trends, corresponding with that they were affected mainly by regional atmospheric Hg deposition. Before ~1950, the records show negligible Hg pollution signals. Atmospheric Hg in the region had increased rapidly since the 1950s, lagged more than a half-century compared to the global Hg. This indicates that they were seldom affected by Hg emissions dominated by Europe and North America after the industrial revolution. The Hg increases since the 1950s in the two records corresponded well with rapid industrial developments in and around Shanxi Province after the founding of the China, implying the dominant contribution of domestic Hg emissions. By comparing other Hg records, we find that widespread increases in atmospheric Hg in China likely occurred post ~1950. This study rouses to re-examine historical variations in atmospheric Hg at various settings, which is significant to understanding global Hg cycling in the industrial era. [ABSTRACT FROM AUTHOR]

To measure and analyze the evolution characteristics and influencing factors of China's industrial green development level is of great significance in achieving carbon neutrality goal. Based on the panel data from 2000 to 2018 of 30 provinces in China, this research uses the super slack-based measuring model and the Malmquist-Luenberger index to calculates China's industrial green total factor productivity and to describe its evolution characteristics using the kernel density function and moreover uses the Spatial Durbin model and the partial differential method to explores its main influencing factors. This study finds that China's overall industrial green development level is not high but shows an upward trend year by year; carbon emissions, fiscal decentralization, and urbanization are not conducive to improving the national industrial green development level, whereas economic development, foreign direct investment, industrial structure, and technological progress are positive contributors. At the same time, the level of economic development and technological progress have significant direct and spatial spillover effect. Our findings also provide some policy implications for improving China's industrial green development. [ABSTRACT FROM AUTHOR]

In order to explore the evolution process of regional vegetable circulation efficiency and its influencing factors, this paper uses Super-SBM model considering unexpected output and GML index to calculate the vegetable circulation efficiency of 13 provinces (cities) in Central China and its surrounding areas from 2015 to 2019, then conducts spatial autocorrelation analysis on the vegetable circulation efficiency in this region through Moran index. Finally, SPDM model is constructed to explore the spatial effect of the influencing factors on the vegetable circulation efficiency in this region. The results show that (1) the vegetable circulation efficiency of most provinces (cities) in this region is low. (2) The Global Moran's I of the vegetable circulation efficiency in this region is positive, that is, the vegetable circulation efficiency in this region shows a certain degree of spatial agglomeration effect. (3) The level of scientific and technological innovation and the degree of government support have significant positive direct and indirect effects on the efficiency of vegetable circulation in the region, the quality of workers has significant positive indirect effects, and the level of economic development and industrial structure have significant negative indirect effects. [ABSTRACT FROM AUTHOR]

Australia is one of the largest nations in the globe in terms of land area and is home to numerous animals alongside unique and unusual climates and immense forests and oceans. Despite having a very tiny population, the nation is an extremely valuable ecological territory. Unfortunately, due to several changes in land use, habitat loss and deterioration—particularly in light of the recent severe bush fires exacerbated by climate change—the environmental issues in Australia have got the attention of many academics. Therefore, this paper seeks to assess the association between Australia's energy use, CO 2 emission, trade liberalization, industrialization and economic growth from 1990 to 2018. An autoregressive distributed lag and a vector error correction model (VECM) are employed to take care of possible endogeneity and the long-run association. Our results demonstrated that economic growth and energy use have positive and statistically significant effects on emissions of CO 2 , but trade liberalization has a significantly adverse influence on emissions of CO 2 both in the long and short term. Granger test in VECM uncovered single-direction Granger interrelationships among trade liberalization and industrialization, as well as among industrialization and carbon dioxide. When attempting to implement effective energy policies, Australian policymakers should first take into account the prominent role played by energy usage and trade liberalization in promoting economic development and impeding environmental health. [ABSTRACT FROM AUTHOR]

Over the past few decades, the development of transport infrastructure has attracted huge local and foreign direct investments to raise the overall industrial output in Pakistan. Meanwhile, the transport sector has emerged as one of the most emissions-intensive industrial sectors. Thus, this study is the first to investigate how Pakistan's transport infrastructure across four modes—roads, trains, ports, and airways—affects industrial value addition and carbon footprints in the country. By making use of the theoretical and empirical literature, the study builds carbon emission and economic output models to determine the economic and environmental sustainability of transport infrastructure development in the country. The empirical findings conclude long-term environmental sustainability issues in transportation infrastructure development. All means of transport infrastructure development have a significant positive impact on carbon dioxide emissions in the country. Only ports and highways, however, provide a positive contribution to industrial output. Additionally, population growth, capital, labor, and urbanization are positively linked with the industrial output and carbon dioxide emission in the country, whereas trade openness helps to offset the emission intensity to some extent. Given this evidence, we provide detailed policy implications by highlighting the significance of greener technologies under new transport-related infrastructure investment that addresses SDG-9. [ABSTRACT FROM AUTHOR]

China's state-level new districts (SNDs) have become the new growth poles for the economic development of the cities where they are located, and a reasonable industrial structure is crucial to industrial sustainable development of SNDs and urban economic growth. This study uses multi-dimensional indicators to measure the convergence level of industrial structure among SNDs and reveals its dynamic evolution trend and formation mechanism. In this context, this study uses dynamic panel model to test the effect of various factors on the convergence of industrial structure. The results are as follows: the advantageous industries in Pudong New District (PND) and Liangjiang New District (LND) are concentrated in capital-intensive and technology-intensive industries. The advantageous industries in Binhai New District (BND) are scattered, and its advantageous industries are distributed in resource-intensive, technology-intensive, and capital-intensive industries. There is an obvious convergence of industrial structure among SNDs, but the degree of convergence of industrial structure among SNDs is different. The result of the regression model shows that the convergence of industrial structure has a significant cumulative effect; investment scale (IS) and government intervention (GI) significantly increase the convergence level of industrial structure, while market demand (MD) and technology level (TL) significantly decrease it. Moreover, the effects of GI and MD on industrial structure convergence are stronger. [ABSTRACT FROM AUTHOR]

Groundwater is a vital resource in arid areas that sustains local industrial development and environmental preservation. Mapping groundwater potential zones and determining high-potential regions are essential for the responsible use of the local groundwater resource. When utilizing machine learning or deep learning algorithms to forecast groundwater potential in arid areas, difficulties such as inaccurate and overfitting predictions might occur due to a shortage of borehole samples. In this study, a database of groundwater conditioning factors with a size of 275,157 × 9 was created in the Qaidam Basin, and 85 known borehole samples were collected. The groundwater potential was evaluated using a combination of rank sum ratio (RSR), projection pursuit regression (PPR) and random forest (RF) algorithms, resulting in four models: PPR, RSR-PPR, RSR-RF, and RF. Results indicated that the groundwater potential was higher in mountainous regions surrounding the Qaidam Basin and decreased progressively towards the central and northwestern regions where most industries and facilities are located. The two primary factors, according to the PPR and RF models, were evapotranspiration (0.246, 0.225) and landform (0.176, 0.294). In terms of their ability to accurately forecast the borehole samples, the four models ranked as follows: RF > RSR-RF > RSR-PPR > PPR. The accuracy of the four models in the low-potential area was 0.73 (PPR), 0.60 (RSR-PPR), 0.87 (RSR-RF), and 0.80 (RF), respectively. However, the RF model showed overfitting due to a lack of samples, especially in high-potential regions, which limits its applicability. The RSR-RF method was applied directly to evaluate the entire factor database, avoiding the risk of overfitting caused by a limited number of training samples. The results demonstrate that the RSR-RF model is effective for classifying groundwater potential types in samples and mapping groundwater potential of the study area. This research presents a novel approach for groundwater potential predictions in areas with insufficient sample sizes, providing a reference for policymakers and researchers. [ABSTRACT FROM AUTHOR]

To achieve low-carbon development of industrial land in China, it is important to coordinate the conflicts of interest among stakeholders in the process of land expropriation and utilization. However, the complex interaction mechanism and influencing factors among stakeholders make it difficult to achieve the goal under the Chinese decentralization and unique land development pattern. To solve these problems, this paper first analyzes the four main stakeholders' conflicts of interest in the process of land expropriation and utilization, that is, the central government, local government, peasant, and enterprise. Then, we construct two evolutionary game models to examine the dynamic changes of stakeholders' different strategies and take the Beijing-Tianjin-Hebei region as an example to compare the impacts of factors on strategies under different conditions using simulation analysis. The research shows that under the Chinese decentralization, adjusting the action strengths of different stakeholders can have different effects on system equilibrium. In terms of the central government's reward and penalty, increasing the reward and penalty for local government will shorten the time of system equilibrium to a different degree, and there is little difference between the effects of political and economic penalties. Interestingly, increasing the incentives for peasants cannot promote the system equilibrium in advance. In addition, the key to local governments' decision on illegal land expropriation lies in benefits rather than costs, and investment in low-carbon technology reform with positive externalities is easier to control than investment in economic production with negative externalities associated with pollution emissions. [ABSTRACT FROM AUTHOR]

As a key strategy to promote system reform, improve the investment environment, and encourage industrial agglomeration, the national high-tech industrial development zone (NHTDZ) policy in China can not only reduce energy consumption through the scale effect but also improve energy efficiency by modernizing industrial structure and fostering technological innovation, thereby alleviating environmental pollution. Existing studies, however, focus solely on the effects of NHTDZ policy on social and economic development, ignoring their impact on the ecological environment, especially carbon (CO2) emissions that contribute to global warming. Thus, this article analyzes a panel data of 285 prefecture-level cities and above in China from 2003 to 2019 to assess the influence of NHTDZ policy on CO2 emissions, treating the NHTDZ construction since 1988 as a quasi-natural experiment. The results indicate that the NHTDZ policy would mitigate urban carbon emissions, particularly in middle, southeastern, medium-sized, resource-based (RB), non-key environmental protection (non-KEP), and non-two control zone (non-TCZ) cities. In addition, the mediation mechanism test demonstrates that the environmental benefits of the NHTDZ policy in China are attributable to the scale effect, the structural upgrading effect, and the technology innovation effect. The NHTDZ policy would lower per capita CO2 emissions by reducing energy consumption, upgrading industrial structure, and promoting green technology innovation. [ABSTRACT FROM AUTHOR]

The debate about the adoption of green technologies (GTs) is trending due to increasing environmental concerns across the globe. In the context of the manufacturing industry, the research about enablers to GT adoption while using the ISM-MICMAC approach is scarce. Hence, the empirical analysis of GT enablers is being done in this study using a novel ISM-MICMAC method. Firstly, the research framework is developed by using the ISM-MICMAC methodology. Then, enablers to GTs were extracted from existing studies and validated by experts. Results showed that the most significant enabler to GT adoption is providing incentives for green manufacturers (1) in the ISM model. So, manufacturing firms must take initiatives for GTs to mitigate the negative environmental impact of industrialization without losing their earnings. This research brings substantial empirical scholarship to understand GT enablers and their contribution in GT enabler incorporation in the manufacturing industry of developing economies. [ABSTRACT FROM AUTHOR]

Developing nations aim to industrialize and grow sustainably often ignoring the environmental consequences. However, few empirical studies have looked at the influence of industrialization-driven economic transition on carbon footprint in developing nations using a non-parametric approach. In this milieu, on the ground of Kaya's identity and the novel multivariate quantile-on-quantile regression (QQR) (extension of Sim and Zhou's (2015) bivariate QQR model), the present research studies the impact of industrial value-added (IGVA), population, energy intensity, and carbon intensity on CO2 emissions in India. This study is one of the first in the literature to evaluate the industrialization-carbonization nexus in the context of Kaya's identity for the Indian economy utilizing an innovative multivariate QQR approach, which makes a methodological and empirical addition to the literature. The outcomes of the multivariate QQR technique demonstrate that economic and environmental development requires continual long-run strategies. The empirical findings revealed that there is no authentication that India's carbon emissions increased due to its industrialization, which exhibited that IGVA has a negative and significant connection with CO2 emissions. In some quantiles, population size positively impacts CO2 emissions. On the other hand, carbonization in the Indian economy is asymmetrically affected by GDP per capita and energy and carbon intensity. The quantile Granger causality study further supported the aforementioned results. The current analysis also offers policy suggestions for environmentally friendly sustainable economic growth and to achieve the sustainable development goals (SDGs) of India. [ABSTRACT FROM AUTHOR]

Although economies have experienced immense growth in recent times, however, it also comes with environmental and social consequences which question the current practices and threaten the well-being of current as well as the future generation. This realization, thus, pushes institutions to bring change in existing energy-related policies in order to incorporate social and environmental concerns. Clean energy transition, in this regard, is gaining attraction all over the world as it shifts away economies from non-renewable resources. The study, thereby, intends to explore the role of governance and environmental taxes in the energy transition in China economy over the period 1999–2019. The roles of industrialization and economic growth in the transition of energy are taken into consideration. The recently introduced legit quantile autoregressive distributed lag (QARDL) model and Granger causality in quantiles are applied to quarterly data spanning 1999Q1 to 2019Q4 for empirical quantile analysis. Results echoed that governance has a positive impact and environmental resources have a negative impact on energy transition across all quantiles. However, economic growth influences clean energy transition only at extremely higher quantiles (0.60–0.95), and industrialization does not have any effect on energy transition over the entire quantile range. The findings of the Granger causality analysis reveal the presence of a bidirectional causal association between clean energy transition and all the variables. Worthy policies are recommended on the basis of the findings. [ABSTRACT FROM AUTHOR]

Maintaining ecological quality of energy use without compromising on economic growth has become the key research agenda of existing literature. Emerging economies are particularly facing this dual problem where they need to look in to the factors which impact sustainable energy consumption. The article, thus, aims to examine impact of economic and non-economic determinants on sustainable energy consumption in Vietnamese context. Factors such as industrialization, population growth, inflation, and employment rate are being considered as economic indicators and eco-innovation and political instability are being used as non-economic indicators. The study has taken secondary data from secondary sources such as Organization for Economic Co-operation and Development (OECD), the global economy, and World Development Indicators (WDI) from 1986 to 2020. The study has applied the Bayesian auto-regressive distributed lags (BARDL) model and the non-linear autoregressive distributed lag (NARDL) technique to check the association among variables. The results revealed that industrialization, population growth, inflation, employment rate, and eco-innovation have a positive linkage with SEC in Vietnam. The results also indicated that political instability has a negative association with SEC in Vietnam. In the light of results, it is obvious that government fiscal and monetary policies must be favorable to inflation so that sustainable energy can be introduced and started to consume. The study also conveys that the policymakers must take care of employment rate growth, for it can encourage sustaining energy consumption. [ABSTRACT FROM AUTHOR]

Carbon emission (CE) reduction has become the primary task of China's urban agglomerations (UAs) in achieving sustainable development goals. This paper uses a decoupling model and coupling coordination model to measure the relationship between the development levels of different types of UAs and CEs in China from 2004 to 2016. Concurrently, the geographically and temporally weighted regression model is used to explore the spatial heterogeneity of the impact of different driving factors on the CEs of UAs. The results show the following: Most UAs have the potential to further decouple CEs and economic growth. Most UAs are still in coordinated development (> 0.5). Among the service innovation UAs, the Yangtze River Delta UA has a coupling coordination of less than 0.3, while the Pearl River Delta UA has a coupling coordination of more than 0.8, showing polarization. Manufacturing and resource-based UAs are still in the grinding adaptation stage (0.5–0.8). There are apparent spatiotemporal differences in the impacts of various driving factors on the CE of UAs. The level of land urbanization and investment in fixed assets promote CEs. However, the level of population urbanization and industrial structure restrain CEs. Therefore, reducing land development and industrial transformation can be an effective means to reduce CEs in UAs. These findings will provide extensive insights for different UAs to achieve differentiated low-carbon development. [ABSTRACT FROM AUTHOR]

There is a broad consensus that information and communication technology (ICT) development contributes to economic growth, but its environmental benefits have not been thoroughly studied. This paper explores the impact and mechanisms of ICT development on manufacturing carbon emissions. We first conducted empirical tests based on panel data from 18 manufacturing sectors in 42 countries from 2000 to 2014. The results show that domestic ICT development reduces manufacturing carbon emissions, but the effect is only significant in sectors with high ICT embedded. Second, technological progress, industrial internal structure upgrading, energy consumption intensification, and low carbonization are the main channels for ICT development and embedding to reduce manufacturing carbon emissions. Third, the carbon emissions from manufacturing, which are deeply embedded by ICT, will decrease as the position of ICT forward GVCs improves, and increase as the position of backward GVCs increases. In addition, ICT development has a more significant impact on reducing carbon emissions in high-tech manufacturing. This paper has enriched research on the environmental benefits of ICT development and has been informative and insightful for countries in formulating industrial development policies and implementing the Paris Agreement. [ABSTRACT FROM AUTHOR]

This study examines how renewable and non-renewable energy generation interacts with both to affect the ecological footprint in China during 1990–2019 by using FMOLS, DOLS, and CCR estimation techniques and ARDL simulation models to assess the impact of industrialization and urbanization on environmental sustainability based on the environmental Kuznets curve hypothesis model framework. Firstly, the findings verify the applicability and validity of the EKC hypothesis in China. Secondly, renewable energy generation, industrialization, and urbanization facilitate the reduction of ecological footprint and the improvement of environmental quality in the long run, while non-renewable energy generation increases the ecological footprint and leads to the intensification of ecological pollution. However, the short-term estimates give evidence that industrialization, urbanization, and renewable and non-renewable energy generation can all increase the ecological footprint, which is not conducive to ecological sustainability. Thus, from the perspective of ecological sustainability in China, our findings are important in that they provide clear directions for ecological policy formulation, and we also provide some targeted policy recommendations for them to promote sustainable development as a goal. [ABSTRACT FROM AUTHOR]

The sustainability of industrial production systems is considered to be the key to promoting green transformation and upgrading of manufacturing industry. This study proposes an emergy-based method for the measurement and evaluation of the sustainability of industrial production systems. The method uses emergy as a measure, expresses all types of inputs and outputs of production systems in terms of solar emergy, and constructs indicators for the sustainable evaluation of industrial production systems that account for economic and environmental benefits from the perspectives of system functions, ecology, and sustainability. This method was applied to a disc cam machining system at a machinery company, where the user was able to quantify the sustainability of the production system and, through feedback, optimise the production process to reduce energy consumption and environmental pollution and improve the sustainability of the production process. The results show that after optimization, the emergy yield ratio of the disc cam machining system is increased from 1.45 to 4.32, the environmental load ratio is reduced by 16%, and the emergy-based sustainability index is increased by 85%. The system has long-term sustainable development capability in the future. This study provides a new theoretical perspective on sustainability assessments of industrial production systems, and our findings provide a scientific basis for guiding the sound operation and sustainable development of industrial production systems. [ABSTRACT FROM AUTHOR]

Not only has artificial intelligence changed the production methods of traditional industries; it has also presented a great opportunity for future industrial development to decouple from environmental degradation and the promotion of green economic growth. The article studies the influence of artificial intelligence on green economic growth and its mechanism. The research shows that (1) artificial intelligence can promote green economic growth in China. After accounting for spatial factors, it was found that artificial intelligence could promote local green economic growth, but had a siphon effect on neighboring green economic growth. From the perspective of dynamic effects, in the short term, artificial intelligence will not significantly dampen green economic growth in neighboring regions. In the long run, artificial intelligence will have a stronger role in promoting green economic growth, and the siphon effect on neighboring cities will be more significant. (2) As the level of human capital increases, the negative spillover effect of artificial intelligence will be significantly weakened. The promotion effect of artificial intelligence on green economic growth is relatively weak in resource-based cities. (3) Artificial intelligence has obvious attenuation characteristics on the spatial spillover effect of green economic growth, but significant influence is limited to within 200 km. (4) Artificial intelligence has the greatest impact on productivity, accounting for 30.59% in promoting green economic growth. The green innovation effect was 0.0181, accounting for 5.64%. The resource allocation effect is 0.0011, accounting for 3.44%. This paper provides policy enlightenment for promoting industrial intelligence and green economic growth. [ABSTRACT FROM AUTHOR]

Around the globe, seawater intrusion in the coastal aquifer is a significant problem. Excessive groundwater extraction because of population growth, industrialization, tourism, and other anthropogenic activities and geogenic processes initiates and accelerates this problem. The contaminated groundwater impacts the health, economic activities, and social and cultural development of coastal regions. This work aims to explore the current status and a holistic comprehending review of geophysical studies applied to delineate the seawater intrusion in the high-quality coastal aquifers in India, as well as its origin and causes, mitigation strategies, and recent advancements in geophysical techniques to access the qualitative and quantitative properties of the complex aquifer system. In the future, it is recommended to do a detailed subsurface imaging of the entire coastal belt of India to decipher the lateral and vertical variation of the lithological conditions and seawater intrusion in space and time with improved/advanced geophysical techniques, which can lead toward sustainable development. [ABSTRACT FROM AUTHOR]

Zhejiang Province is a "demonstration area for high-quality development and construction of common prosperity" in China. Moreover, the county is the basic unit and power source for the economic development of Zhejiang Province. Therefore, the research on the spatial–temporal characteristics and influencing factors of county-level carbon emissions is of great significance for Zhejiang Province to achieve the strategic goal of carbon peak and carbon neutrality. Based on the carbon emissions and socio-economic data of 62 counties in Zhejiang Province from 2014 to 2020, the spatial dependence and agglomeration of county-level carbon emissions are analyzed through the spatial autocorrelation test and local spatial autocorrelation test respectively. According to the spatial–temporal characteristics of county-level carbon emissions revealed by the index of Moran's I and local Moran's I, the spatial error STIRPAT model is used to study the influencing factors of county-level carbon emissions in Zhejiang Province, China. The main results are as follows: (1) The total amount of county-level carbon emissions of 62 counties fluctuates from 259.69 to 326.28 million tons and shows a growth trend. (2) Moran's I index is between 0.369 and 0.399. The county-level carbon emissions have a significant spatial correlation, and the spatial agglomeration trend is relatively stable, which is consistent with the hypothesis of the geographical polarization effect. (3) High-high agglomeration counties are concentrated in the northeast of Zhejiang Province, while low-low agglomeration counties are mainly in the southwest. (4) The relationship between county per capita GDP and carbon emissions has not been "decoupled," because when other variables remain unchanged, the county's total carbon emissions will increase by 2.866% for every 1% increase in the county's per capita GDP; the increase of the proportion of secondary industry contributes to the decline of carbon emissions, and the low-carbon effect brought by large-scale industrial development as well as scientific and technological innovation has not yet appeared. (5) The estimate of the spatial coefficient λ was 0.324, which illustrates that the carbon emission of a single county is positively affected by the carbon emission of the neighboring counties, and other socio-economic factors affecting carbon emission among counties also have a spatial correlation. Therefore, the policy of realizing regional coordinated development as well as the carbon peaking and carbon neutrality goals should not only focus on industrial layout, but also take a dynamic and comprehensive consideration from a spatial perspective. [ABSTRACT FROM AUTHOR]

Industrial ecological efficiency is regarded as an urgent challenge that affects the development of ecological civilization and environmental governance. Here, we propose a data-driven approach to measure and promote regional industrial ecological efficiency. We collected data related to regional industrial development and used the Data Envelopment Analysis-Banker Charnes and Cooper (DEA-BCC) model to measure regional industrial ecological efficiency from a static perspective. The Malmquist index model was then used to measure regional industrial ecological efficiency from a dynamic perspective. In addition, we used a Tobit regression model to identify the factors affecting regional industrial ecological efficiency. Through a case study of regional industrial ecological efficiency, we demonstrate the specific application of the proposed data-driven approach. This study provides a new and effective tool for improving industrial ecological efficiency at a regional scale. This method can help enterprises and local governments improve industrial ecological efficiency, coordinate the relationship between industrial economic growth and the ecological environment, and boost regional efforts to achieve carbon peaking and carbon neutralization goals. [ABSTRACT FROM AUTHOR]

Recently, rural development has depended on the construction industry's success due to the high employment rate in the construction industry and its development role in the rural areas, and this phenomenon needs research focus. Hence, the current article examines the impact of the construction industry (construction industry revenue and growth) and construction policy (construction industry subsidies) on sustainable rural development in China. The study also used the control variable of gross domestic product (GDP) and industrialization. The article has collected secondary data from the Ministry of Housing and Urban–Rural Development and World Development Indicators (WDI) from 1991 to 2020. The article has applied the Augmented Dickey-Fuller (ADF) test to examine stationarity and quantile autoregressive distributed lag (QARDL) model to investigate the association among variables. The results revealed that the construction industry revenue, growth, construction policy GDP, and industrialization positively link sustainable rural development in China. Thus, the findings exposed that if the country's construction industry improved, rural development also increased accordingly. This study guides the policy development authorities to develop effective policies related to improvement in the construction industry that will enhance sustainable rural development. [ABSTRACT FROM AUTHOR]

Urbanization and economic growth in sub-Saharan Africa have undergone dramatic changes in recent decades with countries in sub-Saharan Africa seeking to industrialize their economies to boost economic growth. This study, with panel data from 37 sub-Saharan countries between the time period of 1995 and 2017, employs panel cointegration tests and pooled mean group ARDL (PMG-ARDL) techniques and the Dumitrescu-Hurlin causality test to empirically examine the impact of urbanization, economic growth, energy consumption, and industrialization on carbon emissions. Results from the PMG estimator confirm no significant impact of urbanization on carbon emissions in both the long run and short run. One percent surge in energy consumption was found to have significantly caused 1.03% and 0.15% rise in carbon emissions in both long run and short run respectively, thereby reducing the environmental quality. Further findings revealed a significantly negative relationship between economic growth and carbon emissions as well as industrialization and carbon emissions. Additionally, our causality test shows a unidirectional relationship running from energy consumption to carbon emissions. Policymakers are advised to develop rural areas and plan urban centers, pay attention to sustainable energy sources and waste recycling, and educate the populace to improve environmental quality. [ABSTRACT FROM AUTHOR]

This study has been carried out to analyze the contribution of industrialization and energy consumption by keeping the role of urbanization on environmental pollution for South Asia. The study used Augmented Mean Group (AMG), Common Correlated Effects Mean Group (CCEMG) analysis, Westerlund co-integration test, and Dumitrescu-Hurlin causality test for the panel of South Asia covering the period 1984–2016. To measure the status of environmental pollution, CO2 is taken as a proxy indicator, and industrialization is measured by the industrial value-added. The results of AMG demonstrate that industrialization and energy consumption are significant indicators of environmental pollution and these empirical findings are also confirmed by the CCEMG model. The long-run co-integration between industrialization, energy use, urbanization, capital, and environmental pollution is also confirmed by the Westerlund co-integration test. The findings of the Dumitrescu-Hurlin causality test also confirmed the bidirectional causality between industrialization and pollution. A unidirectional causality is observed from energy consumption to pollution. The study suggests formulating policies for energy-efficient technologies in the industrial sector and the high-speed pattern of urbanization. [ABSTRACT FROM AUTHOR]

Industrial structure upgrading has a spatial spillover effect, which is an important way to accelerate urban green innovation and realize urban sustainable development. Therefore, it is very important to study the impact of industrial structure upgrading on urban green innovation. Using panel data of 260 prefecture-level and above cities in China from 2005 to 2019, this paper studies the spatial effect of industrial structure supererogation on green innovation by using the spatial Durbin model (SDM). The results show that both industrial structure upgrading and green innovation show a trend of regional agglomeration and fluctuation. The direct effect of an industrial structure supererogation on green innovation is significantly positive, while the indirect effect and total effect are significantly negative. The direct effect, indirect effect, and total effect of industrial structure rationalization on green innovation are significantly positive. This study can provide a useful reference for the coordinated development of industrial structure upgrading and green innovation. [ABSTRACT FROM AUTHOR]

In the twenty-first century, exposure to air pollution has become a threat to human health worldwide due to industrial development. Timely, comprehensive, and reliable assessment and prediction of disease burden can help mitigate the health hazards of air pollution. This study conducted a two-stage analysis. First, we reported the air pollution–related disease burden globally and for different subgroups like socio-demographic index (SDI), sex, and age. We analyzed the trend of the disease burden from 1990 to 2019. In addition, we explored whether and how some national indicators modified the disease burden. Second, we predicted the number and the age-standardized rates of death and disability-adjusted life years (DALYs) attributable to air pollution from 2020 to 2044 by the autoregressive integrated moving average (ARIMA) model and exponential smoothing model. The age-period-cohort (APC) model in the maximum likelihood framework and the Bayesian APC model integrated nested Laplace approximations (INLAs) were further applied to perform sensitivity analysis. In 2019, air pollution accounted for 11.62% of death and 0.84% of DALY worldwide. The corresponding age-standardized rate was 85.62 (95% uncertainty interval (UI): 75.71, 96.07) and 2791.08 (95% UI: 2468.81, 3141.39) per 100,000 population. From 1990 to 2019, the number of death attributable to air pollution remained stable, and the number of DALY exhibited a downward trend. The corresponding age-standardized rates both declined. In some countries with larger population densities, higher proportions of elders, and lower proportions of females, the disease burden attributable to air pollution was lower. The predicted results showed that the number of air pollution-related death and DALY would increase. This study comprehensively assessed and predicted the air pollution–related disease burden worldwide. The results indicated that the disease burden would remain very serious in the future. Hence, some relevant policies should be developed to prevent and manage air pollution. [ABSTRACT FROM AUTHOR]

The need to attain lower carbon dioxide emissions has become a topical issue in recent times. The effect of a number of economic variables on carbon dioxide emissions has been empirically assessed. Rising government expenditure, industrialization, and militarization have characterized many developing countries including Ghana. While it is undeniable that such situation has socio-economic importance to offer developing countries, their environmental effects have become a matter of debate among researchers. This study assesses the carbon dioxide emissions effect of industrialization, government expenditure, and militarization in Ghana. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework, the study models Ghana's carbon emission as a function of income, population, industrialization, government expenditure, and military expenditure. Time series data over the 1971–2018 period was used for investigation. The techniques employed to analyze the data were unit root test, cointegration test, and regression analysis. The autoregressive distributed lag (ARDL) regression approach reveals there is an inverted U-shaped relationship between income and carbon emission confirming the environmental Kuznets curve hypothesis. Also in the long run, carbon emissions are positively influenced by population, industrialization, and militarization but reduced by government expenditure. Similar outcome was obtained in the short run. The paper concludes that the level of income, industrialization, militarization, and population matters to deal with carbon dioxide emissions in Ghana. Policy implications of the findings include the urgent need for authorities to promote the use of eco-friendly production methods for military and industrial activities to sustain the economic growth without harming the environment. [ABSTRACT FROM AUTHOR]

This study investigates the influence of globalization, industrialization, urbanization, and financial development on energy demand and environmental quality. The current study uses annual data from 92 Belt and Road Initiative countries over the sample period 1995 to 2018. After the confirmation of cross-sectional dependency and cointegration among the variables, we applied System Generalized Method of Moment, Driscoll and Karry approach, and Dumitrescu and Hurlin causality test to check the long-run relationship and direction of causality, respectively. The results reveal that globalization, financial development, industrialization, urbanization, and economic growth significantly increase the energy demand and CO2 emissions over the sample period. In addition, the granger causality test confirms a one-way causal relationship between financial development and CO2 emissions, GDP per capita and CO2 emissions, and globalization and energy demand. Moreover, there is a two-way causal relationship between urbanization and energy demand and financial development and GDP per capita. Based on these findings, number of suitable policy recommendations presented in the prospective of Belt and Road Initiative countries. [ABSTRACT FROM AUTHOR]

Industry is the lifeblood of the national economy, and it is the inevitable course of sustainability for China to promote the high-quality development of industry. A theoretical framework of industrial development quality (IDQ) was constructed, and 32 influencing factors were selected based on complex system theory. The IDQ in Xi'an from 2004 to 2019 was evaluated, and the influencing mechanisms of each IDQ factor were analyzed through an integrated maximum generalized information entropy (MGIE)-self-organizing feature mapping (SOFM) neural network-decision-making trial and evaluation laboratory (DEMATEL)-interpretive structural modeling (ISM) approach. The results show that the IDQ in Xi'an is on the rise, but the development of internal subsystems is uneven, with the technology subsystem contributing the most and the social subsystem contributing the least. There is a multi-layer hierarchical structure among the influencing factors of IDQ, and the multi-layer structure model can be divided into 10 layers. The first layer is the investment intensity of industrial pollution control, which is the most direct factor that affects IDQ. The tenth layer is the industrial wastewater discharge intensity, industrial waste gas emission intensity, water consumption per unit of industrial added value, highway density, and energy consumption per unit of industrial added value, which are the root factors affecting IDQ. This study can provide a theoretical basis and reference for the promotion of high-quality industrial development. [ABSTRACT FROM AUTHOR]

Resource misallocation has become a new threat to China's high-quality economic development, and industrial intelligence may play an important role in optimizing resource allocation. Using panel data of 30 Chinese provinces from 2010 to 2020, the relationship between industrial intelligence and resource misallocation is studied using the spatial Durbin (SDM) model. To further explore the indirect effect of environmental pollution on resource misallocation, the interaction term between industrial intelligence and environmental pollution is added to test how environmental pollution affects the relationship between industrial intelligence and resource. The research results show that industrial intelligence in China has significant spatial correlation. Local industrial intelligence can improve the local resource allocation, but the intelligence of neighboring industries will intensify the resource misallocation in the region. The interaction between environmental pollution and industrial intelligence also further affects resource allocation, and the interaction term can intensify local capital allocation to improve local labor allocation, and the interaction term in the surrounding area will also improve capital and labor misallocation in the region. Heterogeneous results find that industrial intelligence exacerbates resource misallocation in regions with insufficient capital allocation, and the mediating effect of environmental pollution is not significant. However, in places with excess capital allocation, the development of industrial intelligence helps improve resource misallocation, while the mediating effect of environmental pollution is also present, and industrial intelligence can improve resource misallocation through the indirect effect of improving environmental pollution. Therefore, the policies of local industrial development policies need to fully consider the resource allocation situation in the region. At the same time, it needs to smooth the capital circulation mechanism, reduce the cost of capital allocation, establish a sound employment mechanism, and improve labor misallocation. [ABSTRACT FROM AUTHOR]

Environmental problems not only relate to residents' happiness but also challenge the innovation development of industries. This study first measures the innovation efficiency of China's high-tech industries using the super-efficiency data envelopment analysis model and portrays its spatial characteristics through the Moran's I index and the local indicators of spatial association map. Second, we use the entropy weight method to construct the local living environment index from both natural and social environments. Finally, we utilize spatial econometric models to analyze the impact of local living environment on high-tech industries' innovation efficiency. The results reveal that, first, the spatial variation of innovation efficiency in China's high-tech industries is significant, with efficiency being higher in the east than the Midwest, and higher in the south than the north. Second, innovation efficiency has a positive adjacent and geographical spatial autocorrelation, and low-low agglomeration and low-high agglomeration dominate the types of spatial correlation. Finally, the contribution of the local living environment to the innovation efficiency of high-tech industries is positive and significant. This contribution has an obvious spatial spillover effect and regional heterogeneity. This study can help regional governments to improve local living environments and promote industrial innovation and development. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) emission reduction has become an important concern worldwide. During the past century, human activities have been a significant cause of the increase in the level of greenhouse gases. Past research mainly focuses on evaluating the nexus between unidimensional population factors and CO2 emissions, while few prior studies in a developing country have reported the impact of multidimensional demographic factors on CO2 emissions. As an initial attempt, this study investigates the short- and long-run associations between population factors, low-carbon innovation, and carbon dioxide emissions (CO2) for a panel consisting of 285 cities by employing the pooled mean group (PMG) estimator under the framework of the panel autoregressive distributed lag (ARDL) model. Our main findings are as follows: (1) Population size and population density could increase CO2 emissions, while population quality and low-carbon innovation were essential factors that alleviate carbon emission pressure in the long run. (2) Economic development, foreign direct investment, and industrial development were found to be factors causing the increase in carbon emissions. (3) The split-sample analysis demonstrated that the improvement of population quality still has a positive and significant long-run effect on environmental quality. Simultaneously, low-carbon innovation could realize the enormous dividends of carbon emission reduction in the long run, especially in existing relatively larger CO2 emission areas. Finally, the paper presents important policy implications. [ABSTRACT FROM AUTHOR]

The article titled "Retraction Note: Environmental regulation, renewable electricity, industrialization, economic complexity, technological innovation, and sustainable environment: testing the N-shaped EKC hypothesis for the G-10 economies" has been retracted by the publisher due to concerns identified during an investigation. These concerns include compromised peer review process, inappropriate or irrelevant references, and content that does not align with the scope of the journal. The publisher, in consultation with the Editor-in-Chief, no longer has confidence in the results and conclusions of the article. It is important to note that all the authors disagree with this retraction. [Extracted from the article]

By applying the ARDL (autoregressive distributed lag) bounds testing method, this study examines the short- and long-term dynamic relationship between carbon dioxide (CO2) emissions, economic growth (gross domestic product), industrialization, trade, and urban population in Tanzania from 1990 to 2020. The study found that economic growth, trade, industrialization, and the urban population all contributed to the increase in environmental degradation (i.e., carbon dioxide emissions). However, we found that financial credit (i.e., domestic credit to the private sector) reduces carbon dioxide emissions, and its effects are significant in EKC (environmental Kuznets curve) model. Our findings revealed that economic growth (i.e., income) was responsible for both short- and long-term increases in carbon dioxide emissions in Tanzania. Economic growth is harmful to the environmental quality above a threshold value of 6.23%. Furthermore, the environmental Kuznets curve hypothesis is confirmed for Tanzania. Our findings suggest that policymakers should monitor and use the threshold levels to manage carbon dioxide emissions and to protect the environmental quality. Further, a strong focus should be placed on formulating environmental policies (i.e., carbon tax policy) as industrialization, urban population, economic growth, and trade continue to grow in future, restricting carbon dioxide emissions and safeguarding the environment. [ABSTRACT FROM AUTHOR]