Search Results (148)

China’s national strategies emphasize both achieving carbon peaking and neutrality (“dual carbon” objectives) and fostering high-quality economic development. This dual focus highlights the critical importance of the Green and Low-Carbon Transition (GLCT) of the economy and the development of New Quality Productive Forces (NQPF). Firms are central actors in this transformation, prompting the core research question: How does corporate engagement in GLCT contribute to the formation of NQPF? We investigate this relationship using panel data comprising 33,768 firm-year observations for A-share listed companies across diverse industries in China from 2012 to 2022. Corporate GLCT is measured via textual analysis of annual reports, while an NQPF index, incorporating both tangible and intangible dimensions, is constructed using the entropy method. Our empirical analysis relies primarily on fixed-effects regressions, supplemented by various robustness checks and alternative econometric specifications. The results demonstrate a significantly positive relationship: corporate GLCT robustly promotes the development of NQPF, with dynamic lag structures suggesting delayed productivity realization. Mechanism analysis reveals that this effect operates through three primary channels: improved access to financing, stimulated collaborative innovation and enhanced resource-allocation efficiency. Heterogeneity analysis indicates that the positive impact of GLCT on NQPF is more pronounced for state-owned enterprises (SOEs), firms operating in high-emission sectors, those in energy-efficient or environmentally friendly industries, technology-intensive sectors, non-heavily polluting industries and companies situated in China’s eastern regions. Overall, our findings suggest that corporate GLCT enhances NQPF by improving resource-utilization efficiency and fostering innovation, with these effects amplified by specific regional advantages and firm characteristics. This study offers implications for corporate strategy, highlighting how aligning GLCT initiatives with core business objectives can drive NQPF, and provides evidence relevant for policymakers aiming to optimize environmental governance and foster sustainable economic pathways. Full article

Circular economy is a crucial strategy for achieving sustainable development. The use of solar PV, which is a renewable energy source, has been considered a popular indicator to measure and evaluate the circularity of an economy and enterprises. The recycling of solar PV panels optimises resource use and reduces the need for virgin materials. However, it does not automatically indicate an environmental advantage if the recovering and recycling processes are energy- or emission-intensive. The paper applies life cycle assessment to quantify the material demand for the Italian solar PV sector and contributions of solar PV end-of-life strategies to the enhancement of the circular economy. It is identified that the material intensity of the Italian solar PV sector increases from 4.67 g Sb eq to 5.20 g Sb eq per MWh by 2040 due to the change in technology mix. At the same time, the total material demand, as well as demand for specific materials, increases over the years, from 2008 to 2040. The strategy on recovery, recycling and reintegration of materials slightly reduces the material demand, from 816 tonnes Sb eq to 814 tonnes Sb eq in 2040. It also brings the benefits of reducing all the life cycle impacts, such as greenhouse gas emissions, energy demand, etc. Full article

This study investigates the coupling coordination between carbon emission efficiency (CEE) and carbon balance (CB) in the Yellow River Basin (YRB), aiming to support high-quality regional development and the realization of China’s “dual carbon” goals. Based on panel data from 74 cities in the YRB between 2006 and 2022, the Super-SBM model, Ecological Support Coefficient (ESC), and coupling coordination degree (CCD) model are applied to evaluate the synergy between CEE and CB. Spatiotemporal patterns and driving mechanisms are analyzed using kernel density estimation, Moran’s I index, the Dagum Gini coefficient, Markov chains, and the XGBoost algorithm. The results reveal a generally low and declining level of CCD, with the upstream and midstream regions performing better than the downstream. Spatial clustering is evident, characterized by significant positive autocorrelation and high-high or low-low clusters. Although regional disparities in CCD have narrowed slightly over time, interregional differences remain the primary source of variation. The likelihood of leapfrog development in CCD is limited, and high-CCD regions exhibit weak spillover effects. Forest coverage is identified as the most critical driver, significantly promoting CCD. Conversely, population density, urbanization, energy structure, and energy intensity negatively affect coordination. Economic development demonstrates a U-shaped relationship with CCD. Moreover, nonlinear interactions among forest coverage, population density, energy structure, and industrial enterprise scale further intensify the complexity of CCD. These findings provide important implications for enhancing regional carbon governance and achieving balanced ecological-economic development in the YRB. Full article

Reducing carbon dioxide emissions within carbon-intensive industries is a critical strategy to effectively combat global warming. The accelerated cultivation and enhancement of new quality productivity has created new momentum directed towards industrial low-carbon transformation. Using data from a sample of Chinese provinces and enterprises between 2011 and 2022, this study quantifies, evaluates, and explores the influence and mechanisms of new quality productivity on the low-carbon transformation of carbon-intensive industries. The research findings show that: (1) Fostering new quality productivity effectively promotes the low-carbon transformation of carbon-intensive industries and plays a positive, empowering role. Industrial innovation, digital stimulation, technological innovation, and green empowerment all support the low-carbon transformation of carbon-intensive industries, with their respective impacts gradually decreasing in turn. (2) Mechanism analysis confirms a chain transmission mechanism of “new quality productivity—environmental protection investment—green innovation—the transformation of carbon-intensive industries” at the macro-provincial level. In micro-level carbon-intensive enterprises, a positive U-shaped relationship between new quality productivity and low-carbon transformation of carbon-intensive industries is evident, and the main pathways include increasing low-carbon, energy-saving investment and improving the ESG performance of high-carbon emission enterprises. (3) Advancing transformation is more pronounced in central and western areas, high-carbon areas, non-carbon trading pilot areas, and non-energy-rich ecologically fragile areas. The government and enterprises should take advantage of the development opportunities of new quality productivity and adopt low-carbon behaviors to promote transformational development. Full article

Using 2010–2019 data from 404 listed construction companies in China, we explore the relationship between perception of economic policy uncertainty (PEPU) and energy consumption intensity (ECI) based on a fixed effects model controlling for company, year, and city fixed effects, with standard errors clustered at the industry level. The results show that the perception of economic policy uncertainty reduces construction enterprise energy consumption intensity, and this result holds after a series of robustness and endogeneity tests. Further, this effect is stronger in firms with more green shareholders, environmental information disclosure, and external attention. Moreover, mechanism analysis indicates that internal control enhancement and green innovation improvement, including quantity and quality, are the underlying channels through which the perception of economic policy uncertainty influences energy consumption intensity. Full article

As the climate crisis intensifies, corporate operations face unprecedented challenges from increasing climate risks, necessitating rigorous investigation into their resultant economic ramifications. This study employs text analysis and machine learning methods to construct climate risk perception indicators for a sample of China’s A-share listed energy sector firms (2014–2023). A two-way fixed effects panel model is then applied to study the impact of climate risk perception on corporate performance in the energy industry. The empirical results demonstrate that in China’s energy sector, a 1% rise in climate risk perception corresponds to a 0.104% decline in ROE, mediated through diminished financial flexibility (β = −0.075 **) and elevated R&D intensity (β = 0.649 ***). Moderating effect testing indicates that firms with higher levels of administrative spending effectively buffer against the adverse effects of heightened climate risk perception. Furthermore, this study shows that climate risk perception has more pronounced negative effects on corporate performance in state-owned enterprises (β = −0.113 **), heavily polluting enterprises (β = −0.131 *), carbon-intensive industries, and non-carbon trading pilot regions (β = −0.119 ***). These findings empirically demonstrate how climate risk perception reshapes corporate resource allocation and management, ultimately affecting performance. This study also proposes policy recommendations to enhance corporate climate risk responsiveness, promote technological innovation, accelerate the energy sector’s green transition, optimize corporate capital structure, and advance sustainable development goals. Full article

Transportation infrastructure systems sit at the nexus of urban economic development and emission mitigation. The primary objective is to identify and quantify the key factors influencing CI, with a focus on both the conventional and emerging indicators through an innovative MLP neural network developed using the data of 20 Chinese transportation enterprises that have a business focus on the construction and operation sector from 2018 to 2022. The hypothesis is that integrating unconventional indicators—such as business model entropy and green revenue share—alongside traditional metrics can significantly enhance the predictive accuracy for CI. The results show that business model entropy explains 42.6% of carbon intensity (Cl) variability through green revenue diversification pathways, while emissions trading system (ETS) exposure accounts for 51.83% of decarbonization outcomes via price-signaling effects. The analysis reveals that a critical operational threshold–renewable energy capacity below 75% fails to significantly reduce Cl, and capex/revenue ratios exceeding 73.58% indicate carbon lock-in risks. These findings enable policymakers to prioritize industries with sub-75% renewable adoption while targeting capex-intensive sectors for circular economy interventions. The novelty of this work lies in the application of advanced machine-learning techniques to a comprehensive, multi-source dataset, enabling a nuanced analysis of CI drivers and offering actionable insights for policymakers and industry stakeholders aiming to decarbonize transport infrastructure. Full article

The buffalo species (Bubalus bubalis) is crucial for the global economy, supplying high-nutritional-value animal proteins vital for children’s growth. These animals efficiently convert fiber into energy and thrive in various harsh environments, from frigid climates to hot, humid areas, including wetlands. They produce milk and meat while supporting the sustainability of ecosystems that other ruminants cannot inhabit. Buffalo offers a unique opportunity to supply resources for both rural communities and larger farms located in specific regions, such as marshlands and humid savannahs. They also thrive on extensive pastures and family farms, thus preserving biodiversity, habitats, and cultural practices. Intensive farming brings distinct challenges and is often criticized for its negative effects on climate change. To counter these impacts, multiple strategies have been researched and implemented. These include enhancing livestock genetics, adopting sustainable agricultural practices, optimizing local feed resources (including by-products), managing manure (with an emphasis on renewable energy), and improving animal health and welfare. This review explores various buffalo farming system applications in different global contexts. It is based on the hypothesis that the adaptable traits of buffalo, as well as the environmental and economic challenges that must be addressed for sustainability, are the key factors in determining the viability of such enterprises. Full article

China’s equipment-manufacturing industry accounts for a significant portion of its total carbon emissions. While intelligent equipment optimization has been found to be an effective way of reducing carbon emissions, understanding of its mechanisms remains limited. This paper takes the equipment-manufacturing industry as an example to explore the mechanisms and pathways for enhancing carbon emissions efficiency through intelligent equipment optimization. Using panel data from 243 equipment-manufacturing firms, the analysis identified a nonlinear, U-shaped relationship between intelligent equipment upgrades and carbon emissions efficiency. At the initial stage of intelligent upgrading of equipment, efficiency declines due to the high capital expenditures required for upgrading and integrating advanced systems. However, as these technologies become more integrated into production processes, carbon emissions efficiency improves significantly. This study also examines the mediating role of cost-saving effects and the moderating influence of energy intensity in this relationship. The effect of intelligent transformation on improving carbon emissions efficiency is more significant in high-energy-intensity enterprises. The findings suggest that intelligent equipment optimization not only enhances resource-utilization efficiency but also supports green and low-carbon transitions in equipment-manufacturing enterprises. These insights offer valuable guidance for policymakers and industry leaders aiming to further integrate intelligent manufacturing with carbon reduction strategies. Full article

Millions of tons of slag and clinker can be found in the dumps of enterprises across the Republic of Kazakhstan. The goal of this project is to create a technology that conserves energy in waste treatment. The novelty of the work is the discovery of a new phenomenon, which shows that in the melt layer, there are two reactions opposite in direction and intensity: slow reactions of the decomposition of complex components into simple molecules and rapid responses of the formation of complex components from simple molecules. The dominance of one of the two reactions affects the process’s fuel consumption. Using this phenomenon, a melting reactor was created, which will reduce specific fuel consumption by 3–4 times compared to a Waelz kiln. It is shown that using a new method of CO₂ decarbonization by zinc, it is possible to ensure the production of zinc sublimates and cast stone products and the full neutralization of CO₂. The lowest market potential only for Achisai dump clinker would be around USD 125,600,000 if the cost of commercial clinker sublimates is USD 800/t. The expected net profit would be USD 4,466,039/y. Full article

As global warming and environmental issues become increasingly prominent, steel enterprises, as a carbon-intensive industry, face urgent challenges in energy saving and emission reduction (ESER). This study develops a novel evaluation model integrating the WSR methodology, the cloud matter-element model, and an improved D-S evidence theory to address the fuzziness, randomness, and uncertainty in ESER assessments. A case study demonstrates that this approach can address the correlation between ESER indicators; quantify the evaluation process; and optimize issues related to fuzziness, randomness, and uncertainty. This finding provides a systematic evaluation framework for ESER in steel enterprises operating under the long-process production model (the blast furnace-converter model), offering valuable insights for formulating comprehensive ESER strategies throughout the entire production process. Full article

Promoting the development of low-carbon renewable energy is crucial for meeting the growing energy demand, reducing dependence on fossil fuels, and controlling carbon dioxide emissions. Clarifying the spatiotemporal characteristics of regional renewable energy production and its influencing factors will help optimize the spatial layout of renewable energy production and provide a solid theoretical basis for coordinating the development of all aspects of renewable energy production. Using panel data from 22 districts and counties in Ningxia from 2014 to 2021, this study employed the spatial Gini coefficient, Moran’s I index, standard deviational ellipse, and geographical detector to analyze the spatiotemporal evolution patterns and influencing factors of renewable energy production development in Ningxia. The results indicate that renewable energy production in Ningxia exhibits significant spatial agglomeration and autocorrelation. Temporally, renewable energy production shows a spatial expansion trend characterized by dynamic agglomeration patterns. The coupling degree between renewable energy generation and the spatial distribution of power production is relatively high, with notable regional disparities. Urbanization level, urban population, per capita GDP, and industrial SO₂ emissions have a positive impact on renewable energy production, while energy intensity and environmental regulation show insignificant effects. To further promote the development of renewable energy, Ningxia should strengthen power infrastructure construction at the county level, enhance the radiating and driving effects of high-value areas on surrounding cities and counties, optimize the spatial layout of power facilities based on the agglomeration trajectories of renewable energy production, integrate multiple types of renewable energy to improve overall generation efficiency and system stability, and encourage local enterprises to increase technological and economic investments in renewable energy, thereby advancing sustainable energy transition and achieving high-quality development in resource-based regions. Full article

The presented paper focuses on the current state and evaluation of the integration of environmental innovations from the perspective of the tourism enterprise sector in Slovakia. The quantitative approach herein was conducted by means of a questionnaire survey for the assessment of perceptions about environmental innovations in 144 tourism accommodation companies regarding their importance, attractiveness, usefulness, and financial consequences. These innovations are essential for enhancing business efficiency and competitiveness despite their high costs and intensive maintenance. Important aspects include energy-efficient technologies, clean production technologies, and resource efficiency, which have reduced costs, enhanced corporate image, and improved environmental management. In any case, most obstacles have been linked to economic factors or the need for specialized training for these technologies. This analytical study, using descriptive statistics, semantic differentials, and the Friedman test, found no significant differences among businesses and a strong consensus on the adoption of these innovations to improve operational effectiveness. This research deepens understanding of how environmental innovations transform tourism businesses and contribute to business efficiency. Recommendations for policy and business practices that may successfully support implementation and ensure long-term benefits are presented. Full article

This study categorizes 45 cities into four types based on population dynamics using census data (2000–2020). Methods such as ArcGIS10.8, carbon emission estimation, LISA clustering, and association analysis are employed to explore the spatiotemporal distribution of shrinking cities and carbon emissions. This study analyzes carbon emission patterns and influencing factors for the four city types and provides policy recommendations. The findings are as follows: (1) Lasting-growth cities show a “two-end mass, middle-point” pattern, while stage-growth and stage-shrinking cities are “point” distributed. Lasting-shrinking cities are mainly distributed in the middle and lower reaches of the Yangtze River. (2) Total carbon emissions are rising, showing two clusters of high-value areas. Carbon emission intensity is falling quickly, being higher in the west and lower in the east. (3) Lasting-growth cities have the fastest direct carbon emission growth rate, stage-growth cities have the fastest energy-related indirect emission growth rate, and cities undergoing population increase have the fastest growth rate for other indirect carbon emissions. In terms of carbon reduction, lasting-growth cities perform best, whereas stage-growth cities perform worst. (4) Regional GDP, per capita regional GDP, urban construction area, and hospital beds per 10,000 people promote carbon emission reduction in the four city types, while a higher number of industrial enterprises inhibits it. Birth rate, aging rate, and mortality rate have no significant impact. This study addresses the gaps in previous research on shrinking cities and carbon emission reduction by considering the dynamic nature of shrinking processes and analyzing carbon emission patterns. Full article

The European Union Emissions Trading System (EU ETS) serves as the cornerstone of European climate policy, providing a critical mechanism for mitigating greenhouse gas emissions. Accurate forecasting of the carbon allowance prices within the market is essential for policymakers, enterprises, and investors. To address the need for interval-valued time series modeling and forecasting in the carbon market, this paper proposes a Transformer-based multi-task learning framework that integrates online news and search engine data information to forecast interval-valued EU carbon allowance futures prices. Empirical evaluations demonstrate that the proposed framework achieves superior predictive accuracy for short-term forecasting and remains robust under high market volatility and economic policy uncertainty compared to single-task learning benchmarks. Furthermore, ablation experiments indicate that incorporating news sentiment intensity and search index effectively enhances the framework’s predictive performance. Interpretability analysis highlights the critical role of specific temporal factors, while the time-varying variable importance analysis further underscores the influence of carbon allowance close prices and key energy market variables and also recognizes the contributions of news sentiment. In summary, this study provides valuable insights for policy management, risk hedging, and portfolio decision-making related to interval-valued EU carbon prices and offers a robust forecasting tool for carbon market prediction. Full article