ABSTRACT The development of comprehensive demonstration cities for energy conservation and emission reduction fiscal policies, as a targeted green policy instrument, serves as a critical measure in advancing China’s ‘dual carbon’ goals. Based on data from Chinese A-share listed companies spanning 2006 to 2022, this study employs the establishment of such demonstration cities as a quasi-natural experiment and constructs a staggered difference-in-differences model to systematically examine the impact of these fiscal policies on environmental investment by heavily polluting enterprises. The findings indicate that the policies significantly promote environmental investment by heavily polluting firms, and this conclusion remains robust after addressing endogeneity through methods Mechanism analysis reveals that the policies primarily operate by alleviating corporate financing constraints and enhancing green innovation capabilities. Heterogeneity analysis further shows that the policy effects are more pronounced among non-state-owned enterprises, firms with higher ownership concentration, those located in non-resource-based cities, and enterprises in eastern regions, thereby confirming the differential marginal effects of the policies. This study highlights the positive role of energy conservation and emission reduction fiscal policies in stimulating corporate green investment and fostering value creation, providing valuable empirical evidence to cultivate new green productive forces.

Biofuel mandates and subsidies in several countries led to a 5-fold global growth in ethanol and biodiesel from edible crops in the last two and a half decades. The impacts of this growth for the economy and environment are uncertain and vary with feedstock, production practices, time horizon for impacts, policy parameters, and assumptions inherent to lifecycle assessments and economic modeling. In this context, we offer a perspective on the path forward given that many of the reasons that motivated existing biofuel policies remain relevant. Given advances in batteries and to a lesser extent in green hydrogen, biofuels appear more effective in reducing emissions in applications such as aviation and ocean freight. However, overcoming the lingering technological and economic barriers facing advanced biofuels will require better policies. Both economic intuition and empirical evidence suggest that more targeted approaches can better accelerate innovation and commercialization from waste biomass and dedicated energy crops. Lifecycle-emissions-based performance standards (such as California's Low Carbon Fuel Standard), incentives for emissions reductions and ecosystem services from farming, and policies that minimize regulatory uncertainties (such as relaxation or waiver of annual targets) may lead to technological breakthroughs and adoption of practices that make biofuels more sustainable.

The rapid proliferation of incineration plants has rendered their carbon emissions a substantial contributor to urban carbon emissions. Municipal solid waste classification presents waste-to-energy plants with challenges and opportunities. Monitoring nine Shenzhen incineration plants (2018-2022), we employed Monte Carlo simulations for sensitivity analysis and constructed response surface models to derive quantitative emission reduction pathways. Results indicate the plastic incineration proportion rose to 27.6%, increasing direct emissions and carbon substitution credits from electricity. Reducing plastic by 1% cuts emissions by approximately 17.38 kg CO2-eq/t. Plastic should maintain below 34% considering synergistic effects among dominant emission determinants: plastic composition, grid carbon intensity, and electricity output. Singular interventions targeting plastic reduction or energy efficiency improvements have limited mitigation potential. A comprehensive mitigation scenario combining efficiency improvement, combined heat and power, plastic waste reduction, enhanced recycling, and Bioplastic substitution enables net negative emissions under future low-carbon grids. This study highlights that, systemic transformation from carbon source to carbon sink necessitates coordinated actions across multistakeholder.

The transport industry is still among the most expanding contributors of greenhouse gases in the world with about 23 percent of the total anthropogenic carbon dioxide being emitted by the transport sector with close to 8Gt of CO 2 being emitted each year. The paper will give an extensive discussion of carbon emissions due to transport, modal contribution, and methodology of estimating the emission as well as mitigation methods. As it can be seen, road transport is the leading source of emission, and heavy-duty vehicles make the greatest contribution, despite comprising a small share of the world fleet. More recent innovations in methodology, such as machine learning applications and spatial econometric models have increased the accuracy of emissions estimation and made it possible to implement more specific policy interventions. The paper compares strategies of technological improvement and structural avoid and shift and concludes that the existing complex decarbonisation efforts need to entail vehicle electrification, mode shifting, and demand management as sequential efforts. Results suggest that although electrification has a high potential in emissions reductions, to reach the net-zero transport systems by the middle of the century, combined methods involving technological development and systemic organisational shifts are required.

This study addresses the challenges of coordinating spatio-temporal water allocation to optimize water productivity and reduce carbon emissions in water resource management, particularly the lack of high-resolution, integrated optimization frameworks capable of simultaneously tackling water scarcity and greenhouse gas (GHG) emissions. We propose a modeling approach for large-scale regional rice irrigation that explicitly represents the physical-process-based relationships among irrigation water, yield, and methane (CH4) emissions. Using GIS, a grid-based simulation domain was constructed at a 500 m × 500 m resolution, and the GIS-DSSAT and GIS-DNDC models were employed to simulate yield and CH4 emissions under varying irrigation amounts. The Random Forest algorithm—selected for its ability to capture complex nonlinear interactions—was used to establish the response surfaces linking irrigation water, yield, and CH4 emissions. A spatio-temporal irrigation optimization model was then developed to simultaneously reduce CH4 emissions and enhance water productivity. This methodology was applied to the Sanjiang Plain in Heilongjiang Province, where the NSGA-II algorithm was used to derive optimal irrigation schemes for rice cultivation across 408,264 grid cells. The results revealed quadratic nonlinear relationships between irrigation water amount, yield, and CH4 emissions. Compared to the conventional irrigation practice in the region, which typically involves 15–20 flood irrigation events per season, the optimized irrigation schedule comprised 7–14 events—with 12 events accounting for 42% of the cases—and an irrigation duration ranging from day 137 to 256. This led to a 10.3% reduction in total irrigation volume, a 9.6% decrease in CH4 emissions per unit yield, and a 21.8% increase in water productivity. This study provides valuable decision support for optimizing regional water allocation and developing rice cultivation strategies that improve productivity while reducing emissions.

Global, industrialization-driven environmental bottlenecks push manufacturing enterprises toward green transitions; yet, the information asymmetry between central and local governments, and between enterprises and banks, hinders this process. Adopting a systemic–synergistic perspective integrating decentralized governance and green credit, in this study, we investigate the green transition decisions of manufacturing enterprises. We construct a quadrilateral evolutionary game model involving the central government, local governments, enterprises, and banks, employing MATLAB R2022b to simulate the effects of the key parameters. Subject to the model’s structural assumptions and parameter boundaries, three core findings emerge: first, we find that punitive environmental policies outperform incentive-based instruments in driving enterprise emission reduction; second, we find that the adaptive adjustments made by decentralized governance can effectively facilitate green practices among enterprises; third, within this framework, we find that green credit exerts a non-monotonic impact on enterprises’ green transition behaviors; meanwhile banks’ assessments of enterprises’ environmental risks can indirectly promote enterprise abatement by motivating local governments through signal transmission. This study underscores the systemic synergy of decentralized governance and green credit, offering insights for multistakeholder coordination and policy optimization to advance organizational sustainability transitions for the green economy.

The conflict between rapid industrialization and ecological deterioration constitutes a critical bottleneck for developing regions, particularly concerning industrial wastewater governance. The primary purpose of this study is to investigate whether industrial robotization (IR) can break this deadlock. This study proposes the central hypothesis that adopting IR significantly mitigates industrial wastewater emissions (IWE). Utilizing comprehensive panel data from 30 Chinese provinces from 2013 to 2022, this proposition is rigorously tested using fixed effects models. The main results clearly demonstrate that IR acts as a robust suppressant against IWE. Importantly, mechanism verification shows that this pollution reduction effect is propelled by stimulating green patents and amplifying technical expenditure. The empirical evidence reveals distinct nonlinear features regarding how IR affects IWE. Crucially, heterogeneity analysis indicates that the emission reduction utility of IR becomes significantly more pronounced in territories with robust financial depth and targeted policy backing. Consequently, this study provides vital strategic blueprints for policymakers to leverage industrial automation to navigate the sustainability crisis.

With the rapid adoption of dishwashers in China’s households, the consequent growth in energy and water consumption presents new challenges for energy conservation, emission reduction, and water resource management in China. To address this, this study adopts the concept of the energy–water nexus to analyze the factors influencing dishwasher usage behaviors and quantifies the co-benefits of energy and water conservation through questionnaire surveys, establishing a household dishwasher energy and water accounting model. The findings reveal that dishwasher usage behaviors are influenced by dietary habits—greater oil usage in cooking leads to higher frequency of use and a greater tendency to select intensive wash modes, thereby increasing energy and water consumption. It is projected that by 2030, promoting energy-efficient dishwashers could achieve annual savings of approximately 390 million kWh of electricity and 4 million m3 of water. Furthermore, shifts in dietary habits offer significant potential for achieving co-benefits in energy and water conservation. Encouraging households to adopt low-oil cooking practices could yield additional annual savings of 5.14 billion kWh of electricity and 9.57 million m3 of water. Based on these results, this paper puts forward policy recommendations to facilitate the coordinated management of household energy and water conservation.

Reducing biogas produced by solid waste landfills is a key solution in achieving climate neutrality goals, contributing to GHG emission reduction. This study aimed to investigate the opportunity to invest in a landfill biogas energy production plant when the quality of the biogas (methane concentration) is low. The research was conducted on three municipal solid waste landfills located in Bacău, Ilfov, and Brașov in Romania. Due to improper selective collection and recycling, the average methane content in these landfills is between 7 and 30%. The methodology used to conduct the research combined scientific and digital bibliographic sources, data processing and economic calculations using MS Excel, and the estimation of landfill gas emissions using LandGEM software. The analysis showed sales prices ranging between 155 and 450 [EUR/MWh]. However, the environmental analysis highlights that only the third landfill, with a methane concentration of over 30%, truly contributes to reducing emissions. Also, the use of high quantities of natural gas for energy production is incompatible with the European Union’s climate neutrality objectives. These results demonstrate the need for more efficient technologies or methods for producing and using biogas from waste before it reaches the landfill.

ABSTRACT Traditional hydrogels face limitations in suppressing coal spontaneous combustion due to their reliance on physical mechanisms (heat absorption and oxygen isolation) and lack of chemical inhibition, compounded by intricate preparation procedures. To address these limitations, this study developed a CAA-TA composite hydrogel with synergistic physical-chemical inhibition properties. The hydrogel was synthesized using carboxymethyl cellulose (CMC) as the matrix, acrylamide (AM) and 2-acrylamido-2-methylpropanesulfonic acid (AMPS) as copolymerization monomers, and tannic acid (TA) as an antioxidant. The optimal formulation was determined by optimizing gelation time, water retention, and permeability to meet mine fire prevention requirements. Fourier transform infrared spectroscopy (FTIR) confirmed the successful grafting of AM-AMPS onto the CMC backbone, while hydrogen bonding interactions between the hydroxyl groups of CMC, phenolic hydroxyl groups of TA, and N-H groups of AM contributed to a stable three-dimensional network structure. Scanning electron microscopy (SEM) revealed a dense wrinkled surface and honeycomb-like porous morphology, which enhanced water confinement through physical entrapment and capillary effects, endowing the hydrogel with high water retention and stability. Programmed heating tests highlighted superior low-temperature inhibition, with CAA-TA treated coal exhibiting 52.2% and 56.7% reductions in CO emissions at 100°C and 180°C, respectively, outperforming TA-free counterparts. Fire suppression trials demonstrated rapid temperature reduction from 800°C to 88.9°C within 30 min without re-ignition, surpassing conventional CAA hydrogels. The CAA-TA composite hydrogel achieves efficient coal spontaneous combustion inhibition through a synergistic mechanism combining heat absorption/oxygen isolation and radical scavenging/antioxidant effects. This study advances the design of multifunctional fire-retardant materials through rational integration of physicochemical strategies.

ABSTRACT Establishing low-carbon cities is a crucial strategy for reducing emissions, essential to tackling global climate change, reducing emissions, and achieving green development. This paper evaluates the impact of low-carbon city pilot (LCCP) policy on carbon emissions employing the difference-in-differences method. Our findings show that LCCP policy can significantly reduce carbon emissions. LCCP policy reduces carbon emissions by strengthening government environmental governance, promoting green innovation in enterprises, and raising public low-carbon awareness, with green innovation being the main channel. The carbon reduction effect of LCCP policy is more pronounced in southern regions, cities with high manufacturing concentration, green invention patents, and patents related to digital economy industries. Phased implementation of LCCP policy reduces carbon emissions, demonstrating its promotional effect. Our findings underscore that LCCP policy has long-lasting benefits for emission reduction, offering the important policy guidance for reaching carbon neutrality and fostering the green economy transformation.

ABSTRACT Continuous promotion of energy intensity (EI) reduction in the manufacturing industry is vital for achieving energy consumption and carbon emission reduction goals. This study expands the traditional specialized industrial agglomeration (IA) research perspective and establishes upstream‐IA and downstream‐IA types via industry linkages. Using microscale data for Chinese manufacturing enterprises from 2007 to 2015, through the agglomeration externality channel, we empirically assess the effects and paths of three IA types on enterprise EI and find the following: First, the specialized‐IA, upstream‐IA and downstream‐IA types significantly reduce enterprise EI, and the upstream‐IA type yields the greatest efficiency enhancement effect. Second, the analysis of the mechanism underlying agglomeration externalities reveals that the paths through which the specialized‐IA type affects EI are labour pooling, intermediate input sharing and knowledge spillover; the paths through which the upstream‐IA type operates are intermediate input sharing and knowledge spillover; and the path through which the downstream‐IA type operates is labour pooling. Third, the effect of IA is particularly significant in the sample of nonexporting enterprises and energy‐intensive enterprises. Fourth, the specialized‐IA, upstream‐IA and downstream‐IA types reduce EI without reducing production, thus achieving synergy between efficiency enhancement and consumption reduction.

Lung transplantation is conspicuously resource intensive, yet the carbon footprint of solid organ transplantation, and lung transplantation in particular, remains relatively unmapped. Moreover, there is growing evidence that climate change adversely affects lung transplant recipients, directly and indirectly. This review explores opportunities to integrate sustainable practices across the lung transplantation pathway, with the dual aims of reducing environmental impact and reducing recipient morbidity and mortality. Published literature outlines mitigation strategies to reduce the drivers of climate change, and adaptation strategies to modify the hazardous effects of the climate crisis on healthcare services. Opportunities to create "green chains" of transplant care start with appropriate recipient and donor selection, with both elements optimised to maximise operative success and avoid donor organ discard. The peri-operative phase offers scope for decarbonisation via the reduction of anaesthetic gas emissions, green procurement, and waste reduction. In the post-transplant phase, enhanced recovery after surgery (ERAS) programmes feed into longer term surveillance and care delivery, with increasing opportunities for telemedicine and noninvasive allograft monitoring, to reduce travel-related emissions. The co-ordinated adoption of low-carbon, sustainable practices within lung transplantation offers a meaningful opportunity to reduce environmental harm, enhance healthcare infrastructure and workforce resilience, and improve patient outcomes in a patient cohort vulnerable to climate change. Lessons can be drawn from the global surgery movement; however, it remains the responsibility of transplant practitioners to foster a culture of environmental advocacy and to implement systematic measurement and targets for climate change and health indicators.

Traditional markets serve as key nodes in food distribution systems and have the potential to generate greenhouse gas (GHG) emissions through electricity consumption and waste generation. However, the contribution of traditional markets to carbon footprints has received limited scholarly attention. This study analyzes the carbon footprint of Pasar Mangkang in Semarang City as a basis for developing emission reduction strategies for traditional markets. A quantitative approach was employed through surveys of trader characteristics, calculations of electricity consumption, and measurements of waste generation. Emissions from electricity use were estimated using the Jamali emission factor, while emissions from waste were analyzed based on waste composition and methane generation potential. Data on traders’ behavior and market operational conditions were incorporated to enrich the analysis. The results indicate that total emissions from electricity consumption reached 27.97 tons CO₂ per year, with the largest contribution originating from wet commodity traders who intensively use refrigeration equipment. Meanwhile, total emissions from market waste generation amounted to 139.19 tons CO₂ per year, with waste composition dominated by organic waste (75%), which has the potential to generate CH₄ emissions if not properly managed. Source separation practices remain limited, and the use of single-use plastics is still prevalent. Trader characteristics, commodity types, and waste management behaviors influence the magnitude of emissions generated. These findings demonstrate that traditional markets make a substantial contribution to emissions from the energy and waste sectors but have not yet become a focal point in local climate mitigation agendas. The establishment of an emissions baseline provides a foundation for low-carbon market management strategies. The carbon footprint of Pasar Mangkang is primarily driven by electricity consumption and the high proportion of organic waste. Strengthening energy management, improving waste segregation, reducing plastic use, and enhancing behavioral awareness are key strategic steps toward developing low-emission traditional markets.

Existing research on virtual power plants (VPPs) has not fully integrated the coupling relationships among electricity, heat, hydrogen, and carbon, and scheduling strategies under uncertainty conditions remain imperfect. To address this gap, this paper proposes an optimization scheduling model for a multi-energy virtual power plant (MEVPP) that incorporates uncertainty constraints and multi-energy coupling characteristics. The proposed model integrates biomass co-combustion carbon capture power plants (BCCPP), power-to-ammonia (P2A), and low-carbon chemical production (urea synthesis) within a unified stochastic VPP scheduling framework, achieving multi-energy synergy and flexible coupled operation involving electricity, heat, hydrogen, and carbon. A scenario generation method based on Latin hypercube sampling (LHS) is adopted to formulate a stochastic scheduling model aimed at maximizing the expected total system revenue under wind and solar uncertainties. Simulation results demonstrate that compared to the baseline scenario without carbon capture, the proposed model reduces CO₂ emissions by 38.5% (from 10,000 t to 6,150 t) and total costs by 75.1% (from $800,000 to $199,200) in the optimal scenario. Carbon trading price sensitivity analysis shows that emission reductions can reach 30-38% through constraint adjustments. These findings provide practical insights for system operators and policymakers in advancing low-carbon energy transitions, particularly for China's dual-carbon goals.

Abstract Comprising eight nations and over one-fifth of the world’s population, the South Asian Association for Regional Cooperation (SAARC) is an important bloc. Its green-economy transition relies on coordinated efforts by national and local governments, private firms, community groups, and international agencies, and is supported by renewable-energy incentives, carbon-pricing mechanisms, sustainable land-use policies, and green-finance initiatives. This study aims to identify effective strategies and policy recommendations that support economic sustainability and carbon neutrality in the SAARC region through a thorough analysis of the causal relationships between economic indicators and carbon emissions. The study utilizes the Panel cointegration tests (the Kao test and the Pedroni tests), and the Panel Autoregressive Distributed Lag (ARDL) approach to examine the interconnections between economic growth, use of renewable energy, social entrepreneurship, and carbon emission in SAARC countries. The current study aims to examine the short-term dynamics and long-term equilibrium between important variables like Gross Domestic Product (GDP), natural resources (NR), globalization index (GI), industrial structure (IS), renewable energy consumption (REC), and carbon dioxide emissions (CO₂). Our results show that a 1 percent increase in globalization raises GDP by 2.61 percent, a 1 percent increase in the sustainable development index raises GDP by 0.10 percent, and a 1 percent increase in industrial structure raises GDP by 0.56 percent. Also, a 1percent increase in natural resources causes CO₂ emissions to go up by 0.057 percent in the long term, while a 1 percent rise in globalization and industrial structure causes CO₂ emissions to go up by 0.278 percent and 0.222 percent, respectively. The results show that REC and carbon emissions are inversely related to each other, suggesting that a 1 percent increase in REC may lead to a long-term reduction in CO₂ emissions of 0.316 percent. Our findings imply that SAARC policymakers should boost REC, realign industrial structures, and implement carbon‐pricing mechanisms to drive economic growth while achieving carbon neutrality. With the help of these findings, policymakers can make informed choices that will advance sustainable development and help the SAARC nations become carbon neutral. Graphical Abstract

This study presents results from a spatial modeling and analysis process of six different air pollutants measured over a ten-year period at up to forty-three monitoring stations located in the three provinces of the Basque Autonomous Community (BAC) (Spain). The main objective was to generate detailed maps showing the evolution of these pollutants that cover the entire area using geostatistical techniques. These maps are intended to serve as a basis for both short-term and medium-term environmental studies, while also examining how pollutant levels have changed before and after the COVID-19 pandemic era. Additionally, the paper explores the factors that may explain the differences observed during these two periods. To further analyze the spatial patterns, the study employs the Fuzzy C-Means clustering algorithm to partition the region into four distinct zones based on the concentrations of key pollutants: PM10, NO2, and O3. These pollutants were selected due to their high sampling density, spatial coverage, and complementary sources (traffic emissions, combustion processes, and ozone photochemistry), making them representative indicators of the region’s atmospheric state. The findings reveal significant changes in air quality during the COVID-19 pandemic, particularly in NO2, Benzene, and CO levels, which sharply declined due to reduced vehicular traffic. However, the behavior of PM10 and O3 was more complex, influenced by diverse sources and atmospheric chemistry. The reduction in NO2 emissions during lockdowns led to a counterintuitive increase in O3 concentrations in some areas, highlighting the non-linear responses of atmospheric systems to emission changes. This study underscores the importance of multi-faceted air quality management strategies that account for the intricate interplay of different emission sources and atmospheric processes. The insights gained from this unique period should inform the development of more effective, evidence-based air quality policies for a healthier and more sustainable future.

Modern industrial environments are evolving into data-intensive cyber-physical systems that require robust computational frameworks for performance prediction and optimization. While existing literature has addressed developments in statistical methods, artificial intelligence, and quantum computing individually, there remains a lack of systematic reviews examining the integrated evolution and data processing capabilities of these three paradigms. This review addresses the need to clarify the capabilities, limitations, and application domains of each approach to enable engineers to select appropriate data-driven methodologies for specific optimization challenges. In this review, we traced the historical development of optimization methodologies from design of experiments and response surface methodology through neural networks and generative models to variational quantum algorithms, presented chronological development tables documenting key milestones in each paradigm, and analyzed industrial implementation cases including conversion rate increases and emission reductions. The analysis reveals that statistical methods exhibit unique strengths in systematic data analysis, AI in complex pattern recognition, and quantum computing in high-complexity simulation, with their hybrid integration providing optimal performance. This study provides significance in offering a comprehensive framework necessary for connected industries to strategically deploy multi-paradigm optimization strategies within integrated network environments to achieve sustainability goals while maintaining global competitiveness.

This document gives a review about carbon capture and storage. Given the severe situation of global climate change, the role of CCS technology in this challenge, and the fact that emission reduction will be particularly difficult for some industrial products that are hard to reduce, such as steel and cement. Article introduces various kinds of ways to trap co2, like post combustion capture, and oxyfuel combustion technology, which is applied and superior in some different cases. In addition, the latest progress in novel materials such as solvents, adsorbents and membranes for improvement has been introduced. And then, document continues, there's some other type of storage like the geological or the minerals storage. However, the large-scale application of CCS technology still faces many difficulties. Technological problem as for how there will be high energy consumption, wear and tear and also a problem regarding the safety while storing. Economic and social problems are related to policy uncertainty, high cost, and acceptance by the public. Therefore, CCS could happen, it also has a journey with policy change as well as public knowledge.

The sintering process is undergoing a transformation to meet future challenges, so a more profound understanding of the potential influence of the individual recirculation gas constituents, in particular O 2 , CO, and H 2 O, is essential. Within this paper, miniaturized lab‐scale sintering experiments are presented using an industrylike raw mixture, to study the effects of variations in the individual O 2 , CO, and H 2 O supply on the sintering process and the sinter quality. As the O 2 content (up to 30 vol%) in the suction gas increases, both sintering yield and strength increase, resulting in lower return rates, with more or less constant amounts of CO in the off‐gas. Productivity and sintering strength increase with increasing CO concentration, while the NO concentration in the off‐gas tends to be lower. With increasing H 2 O content, productivity and sintering strength increase with a significantly lower CO concentration in the off‐gas, due to the water‐gas shift reaction taking place in the flame front. The results demonstrate the potential of adjusting the raw mixture recipe by selectively modifying the recirculation gas, particularly towards a lower coke breeze content. This would enable reductions in the specific emissions of CO, CO 2 , SO 2 , and NO per ton of product sinter.