Environment, Development and Sustainability最新文献

Food production systems are faced with increasingly emerging pressures. Worldwide affairs like the Russia-Ukraine war and Covid-19 have raised our concerns about the ability to maintain a steady food supply at a stable price. Food security remains a problem to be addressed, especially taking the growing global population into consideration. This study aims to contribute to global food security by exploring the coupling relationship between resilience and sustainability of China’s food production system. An evaluation system to measure the elasticity and sustainability of China’s food production system was established, and the comprehensive evaluation value, coupling coordination degree, coupling coordination degree and coupling coordinated development type were quantitatively analyzed using entropy evaluation, comprehensive evaluation index model, coupling coordination model and related development level model. The results of our study are as follows. First, the level of resilience and sustainability of China’s food production system fluctuates and is generally on the rise. Second, the coordination level of internal coupling between the resilience and sustainability of China’s food production system is generally on the rise, but the degree of coupling coordination is still at a low level in some years. Third, in terms of comparative development, the resilience of China’s grain production system lagged behind its sustainability, and it only reached a state of synchronous development in 2019. The research findings will provide guidance to the adaptation between the resilience and sustainability of the Chinese food production system and inspire the formulation of related policies.

Carbon emission reduction is a critical objective for enhancing ecological and environmental quality. The shift toward green and sustainable practices is becoming increasingly central to the future development of data centers. Despite its importance, few studies have examined the impact of green data centers on carbon emissions. Based on the event of green data center pilots at district-county level in China, this paper explores the impact of green transformation of data centers on corporate carbon emission reduction and its mechanism of action by using a high-dimensional fixed-effects model with the help of a panel data of A-share listed companies in Shanghai and Shenzhen, China, from 2008 to 2021. The findings reveal: (1) The green transformation of data centers significantly promotes corporate carbon emission reduction. This result is robust, persisting even after adjusting for the influence of other policies and benchmark variables. (2) The study identifies that green transformation substantially enhances the level of breakthrough innovation within enterprises, which in turn significantly reduces their carbon emissions. Additionally, the level of green concern within a company positively moderates the relationship between green transformation and carbon emission reduction. (3) Heterogeneity analysis indicates that the effects of data center green transformation on carbon emissions vary significantly between central and western regions and non-interprovincial border areas. This research provides empirical evidence and policy recommendations to assist developing countries in balancing economic development with carbon emission reduction objectives.

The goal of this study is to thoroughly analyze the connection between green finance (GFI) and agricultural high-quality development (AGHID), with a focus on the use of financial technology (FinTech). This project’s objective is to offer a strong framework of reference for the promotion of agricultural modernization as well as the encouragement of AGHID. Using data from interprovincial panels between 2011 and 2019, this study investigates the role of FinTech and uses the generalized method of moments model to show the effect of GFI on the AGHID. The study's findings indicate that advances in GFI have a statistically significant effect on the AGHID, with significance levels at least reaching the 1% mark. FinTech’s contribution to the area of GFI concerning the AGHID is typically underappreciated. A 1% upswing in the level of GFI corresponds to an average 18.9% increase in the level of AGHID. The study investigates local discrepancies and reveals subtle differences in the ways that GFI influences the AGHID in different regions. All regions benefit from the AGHID when it comes to GFI, although it is most noticeable in the western region. A temporal analysis demonstrates that throughout the 13th period of the Five-Year Plan, the positive impact of GFI on the expansion of premium agriculture was clearly increasing. Further analysis reveals that the information channel impact and the human capital effect are crucial channels via which GFI contributes to the AGHID, providing further insight into the working processes.

Oily-biological sludge generated in large quantities from oil refineries' wastewater treatment plants poses a significant environmental hazard. This study investigates the potential of converting this sludge into energy through anaerobic digestion, producing biogas. Given the sludge's suboptimal carbon to nitrogen (C/N) ratio, sugarcane bagasse, which has a higher C/N ratio, was co-digested with the sludge to enhance biogas yield. Batch co-digestion tests were conducted under thermophilic conditions (55 °C, pH 6.5–8.0, and 60 RPM mixing) to determine the best C/N ratio for biogas production. The raw materials underwent mechanical and thermal-chemical pretreatment using Sodium Hydroxide to improve digestion efficiency. Sugarcane bagasse was treated with 1% Sodium Hydroxide at a 1:10 solid-liquid ratio, 100 °C, and 150 RPM for one hour, while the oily-biological sludge was treated with 1 g/L Sodium Hydroxide under the same conditions. Results from a 33 day batch experiment indicated a positive correlation between increased C/N ratio and biogas yield. The highest biogas and methane yields were at a C/N ratio of 30.0, achieving 202.71 mL of biogas and 76.25 mL CH4 per gram of volatile solids removed. These yields were 72.57 and 77.26% higher, respectively, compared to those at a C/N ratio of 20.0.

Compact city is considered an important planning approach to alleviate carbon emissions (CE) and achieve “carbon neutrality”. However, previous studies tended to intensively investigate the relationship between unidimensional urban compactness (urban form) and household carbon emissions (HCE). In fact, compact city is not only physical compactness but also economic, population, land use and infrastructure compactness. Therefore, the article constructed a multidimensional compactness index system to comprehensively investigate the influence of compact city on HCE in 286 cities in China, using DMSP-OLS/NPP-VIIRS nightlight data and socioeconomic data from 2000 to 2015. The Geographical and Temporal Weighted Regression (GTWR) model is employed in the study to examine the localized impact of different levels of compactness on HCE. The results found: (1) Economic compactness has a promoting effect on HCE in resource-endowed cities with well developed heavy industry and abundon fossil fuels, while economic compactness inhibits HCE in cities dominated by service and high-tech industry. (2) Cities with higher population carrying capacity are more easily to reduce HCE when cities become more compact. (3) For cities with large land carrying capacity and well-built road transportation facilities, the greater the compactness of land use, the more conducive to restraining an increase of HCE. (4) With the rational layout of infrastructure construction, the more compact the infrastructure, the more conducive to restraining the increase of HCE.

With the growing attention on carbon neutrality, the transformation to low-carbon production is the most pressing global mission today. The Environmental Kuznets Curve (EKC) is frequently used to develop carbon neutrality roadmaps for various industries and even entire nations, but few scholars have applied it to the iron and steel industry (IaSI). According to the International Monetary Fund, the global IaSI accounts for 7% of total CO₂ emissions, making it a key sector for emissions in manufacturing. Given the high industrial linkages of the IaSI, it’s crucial to focus on its CO₂ emission patterns. This paper investigates the EKC hypothesis in the IaSI using data from 30 countries from 1990 to 2019. The results show that the EKC hypothesis is valid in the global IaSI. The study suggests that CO₂ emissions of the IaSI will peak when the per capita real GDP reaches $17,535 (constant price in 2010). Unlike emerging economies, the IaSI in advanced economies has reached the carbon peak. The robustness of this result is verified by an appropriate U test. Our results also show that a 1% increase in crude steel production will increase the CO₂ emissions of the IaSI by 0.675%; however, expanding the proportion of using electric arc furnaces to produce crude steel can substantially reduce CO₂ emissions. Discussions on the EKC curve of IaSI reveal significant policy implications for countries striving to achieve carbon peaking and neutrality targets.

The time of carbon peak for major energy-consuming industries determines whether China can meet its carbon peak commitment. Therefore, studying the carbon emissions of major energy-consuming industries is necessary. However, few studies have translated China’s carbon peak goal into the goal of major energy-consuming industries. Using the emission factor method and the Kaya-LMDI model to account for and decompose carbon emissions from 1999 to 2020 and using scenario analysis and the Monte Carlo algorithm to predict the trend of carbon emissions from 2021 to 2030 under different scenarios, we drew vital conclusions. Reduction of energy intensity of production industries and the shift of economic structure to tertiary industry will inhibit the growth of carbon emissions from major energy-consuming industries, with average annual contributions of − 23.27% and − 36.94%, respectively. The growth of per capita industry output and total population will promote the growth of carbon emissions, with average annual contributions of 83.45% and 6.55%, respectively. Production and Supply of Electric Power and Heat has the greatest carbon reduction potential. The energy-saving scenario is most likely to achieve the carbon peak goal, with a carbon peak date of 2028 and carbon emissions from major energy-consuming industries of 13,773 Mt . This means that China will need to make more efforts. This study provides a unique research perspective on carbon peak at the industry level in China, offering new insights into resource allocation and policy preferences, and serving as a reference for other countries aiming to translate carbon peak goal from the national level to the industry level.

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Cities have been shown to exhibit empirical scaling behavior where numerous variables of urban performance are allometric, like greenhouse gas emissions. Polluting emissions have negative environmental and health impacts. This paper will elucidate the empirical urban scaling of atmospheric emissions for the Israeli urban system. It has been shown that cities may be environmentally efficient with CO₂ emissions that seem to be sub-linear, so large cities are more “green”. However, other reports suggest a super-linear relationship with respect to population size, so the large cities are less “green”. We report here for the first time the results of the nonlinear allometric power-law properties of multiple air pollutants, expanding the analysis to include electricity consumption and atmospheric emissions of CO₂, NO_x, SO₂, CO, NMVOC, PM₁₀, PM_2.5, Benzene and 1,3-Butadiene together in one study in the case of Israel. They show the recurring mathematical patterns of cities similar to those reported elsewhere. Electricity usage is super-linear. Pollutant emissions of these greenhouse gases tend to exhibit significant super-linear dynamics (β > 1), though NMVOC and Benzene were linear. These results were conserved when regressing against the urban vehicle fleet size. This evidence supports the hypothesis that large cities may be less “green”. Indeed, different urban characteristics such as geography, local climate and weather conditions, population density, may also affect the pollution levels of cities. Taken together these results give evidence to the effect of urban agglomerations on the environment.

The automotive business is growing continuously along with the global economy. One way to lessen environmental pollution in recent times is to look for clean energy to replace traditional fossil fuels as the vehicle’s power source. This is because there is a lack of environmental energy among other issues. This manuscript proposes an Energy Management Strategy of Fuel Cell Hybrid Electric Vehicles. The proposed hybrid technique is the joint execution of both the Giant Trevally Optimizer (GTO) and Hierarchically Gated Recurrent Neural Network (HGRNN). Hence, it is named as GTO-HGRNN technique. This proposed method’s principal objective is to reduce hydrogen use and raise battery longevity. The proposed GTO approach is used to optimize the DC/DC converter parameter and fuel consumption and the HGRNN approach is used to predict the optimal parameter of the DC/DC converter parameter. By then, the MATLAB platform has the proposed method been implemented, and the existing method is used to compute the execution. Better outcomes are shown by the proposed strategy in all existing systems like Genetic Algorithm, Global Optimisation Algorithms, and Particle Swarm Optimization. The existing method shows hydrogen consumption of 0.4%, 0.3%, and 0.2% the proposed method shows a hydrogen consumption of 0.1% which is lower than another existing system. The existing method shows the cost of 14.90$, 15.90$, and 16.90$ the proposed method shows the cost of 13.90$, which is lower than another existing system.

The pursuit of urban green innovation efficiency (UGIE) has emerged as a pivotal driver for attaining green economic growth. It is crucial to improve UGIE through the producer services agglomeration (PSA) for China. Based on the perspective of spatial correlation, we investigate the spatial spillover effect, mechanism, and nonlinear characteristics of PSA on UGIE. The results indicate that: (1) China’s UGIE exhibits an upward trend with fluctuations, with growth poles in the Guangdong-Hong Kong-Macao Greater Bay Area, Yangtze River Delta, and Bohai Rim. China’s PSA initially declines, gradually increases, and then decreases. There is still room for progress in both UGIE and PSA. (2) The direct and spatial spillover effects of PSA on UGIE exhibit a U-shaped pattern, initially inhibiting and then promoting. However, the spatial spillover effects are only observed within a geographical range of 350 km. Cities in the eastern region or with advanced high-end producer services have lower inflection points. (3) PSA can enhance UGIE in local cities by facilitating knowledge spillover and optimizing labor allocation, while also impacting UGIE in neighboring cities through improved capital allocation. (4) Under varying levels of industrial agglomeration and marketization, the nonlinear impact of PSA on UGIE exhibits a single threshold effect and a double threshold effect, respectively. When the threshold value of PSA reaches 0.548 and the threshold values of marketization reach 8.269 and 11.373, the impact of PSA on UGIE gradually transitions from inhibition to promotion. This study provides insights for rationalizing the layout of urban producer services to enhance green innovation.