Search Results (7,483)

In modern agriculture, tractors play a crucial role in powering tools and implements. Proper operation of agricultural tractors in mechanized field operations can support sustainable agriculture and reduce emissions of pollutants such as carbon dioxide (CO₂). This has been a recurring concern associated with agricultural intensification for food production. This study aimed to evaluate the optimization of tractor gears and engine speed during crop operations to minimize CO₂ emissions and promote sustainability. The experiment was conducted using a strip plot design with subdivided sections and six replications, following a double factorial structure. The first factor evaluated was the type of agricultural implement (disc harrow, subsoiler, or sprayer), while the second factor was the engine speed setting (nominal or reduced). Operational and energy performance metrics were analyzed, including fuel consumption and CO₂ emissions, travel speed, effective working time, wheel slippage, and working depth. Optimized gear selection and engine speeds resulted in a 20 to 40% reduction in fuel consumption and CO₂ emissions. However, other evaluated parameters remain unaffected by the reduced engine speed, regardless of the implement used, ensuring the operation’s quality. Thus, optimizing operator training or configuring machines allows for environmental impact reduction, making agricultural practices more sustainable. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

Urban parking around the world faces similar challenges of inadequate space, pollution, and carbon emissions. Although various smart parking technologies have been tested and implemented, they primarily aim to reduce the time spent searching for parking, without considering the impact on air quality. In this study, the air quality in three urban garages was investigated with portable instruments at the entrance and exit gates and inside the garages. Garage emissions measured include CO_2, PM_2.5, PM₁₀, NO_2, and total VOCs. The results suggested that the PM_2.5 levels in these garages tend to be higher than the ambient levels. The emissions also exhibit seasonal variations, with the highest concentrations occurring in the summer, which are 20.32 µg/m³ in Campus Green, 14.25 µg/m³ in CCM, and 15.23 µg/m³ in Washington Park garages, respectively. PM_2.5 measured from these garages is strongly correlated (with an R² of 0.64) with ambient levels. CO₂ emissions are higher than ambient levels but within the indoor air quality limit. This suggests that urban garages in Cincinnati tend to enrich ambient air concentrations, which can affect garage users and garage attendants. Portable sensors are capable of long-term emission monitoring and are compatible with other technologies in smart garage development. With portable air sensors becoming increasingly accessible and affordable, there is an opportunity to integrate these devices with smart garage management systems to enhance the sustainability of parking garages. Full article

Metro carriages, as enclosed transport microenvironments, have been understudied regarding pollution characteristics and health risks from ACs, especially during high-temperature summers that amplify exposure. This study applied NTS techniques for the first time across three major Chengdu metro lines, systematically identifying sixteen ACs, including hazardous species such as acetophenone, benzonitrile, and benzoic acid that are often overlooked in conventional BTEX-focused monitoring. The TAC concentration reached 41.40 ± 5.20 µg/m³, with half of the compounds exhibiting significant increases during peak commuting periods. Source apportionment using diagnostic ratios and PMF identified five major contributors: carriage material emissions (36.62%), human sources (22.50%), traffic exhaust infiltration (16.67%), organic solvents (16.55%), and industrial emissions (7.66%). Although both non-cancer (HI) and cancer (TCR) risks for all population groups were below international thresholds, summer tourists experienced higher exposure than daily commuters. Notably, child tourists showed the greatest vulnerability, with a TCR of 5.83 × 10⁻⁷, far exceeding that of commuting children (1.88 × 10⁻⁷). Benzene was the dominant contributor, accounting for over 50% of HI and 70% of TCR. This study presents the first integrated NTS and quantitative risk assessment to characterise ACs in summer metro environments, revealing a broader range of hazardous compounds beyond BTEX. It quantifies population-specific risks, highlights children’s heightened vulnerability. The findings fill critical gaps in ACs exposure and provide a scientific basis for improved air quality management and pollution mitigation strategies in urban rail transit systems. Full article

The mining industry (MI) in mineral-rich regions is pivotal for economic growth but is challenged by significant pollution and emissions. This study examines Guangxi, a representative region in China, in light of the country’s “Dual Carbon” goals. We quantified carbon emissions from the MI from 2005 to 2021, employing the generalized Divisia index method (GDIM) to analyze the factors driving these emissions. Additionally, a system dynamics (SD) model was developed, integrating economic, demographic, energy, environmental, and policy variables to assess decarbonization strategies and the potential for carbon decoupling. The key findings include the following: (1) Carbon accounting analysis reveals a rising emission trend in Guangxi’s MI, predominantly driven by electricity consumption, with the non-ferrous metal mining sector contributing the largest share of total emissions. (2) The primary drivers of carbon emissions were identified as economic scale, population intensity, and energy intensity, with periodic fluctuations in sector-specific drivers necessitating coordinated policy adjustments. (3) Scenario analysis showed that the Emission Reduction Scenario (ERS) is the only approach that achieves a carbon peak before 2030, indicating that it is the most effective decarbonization pathway. (4) Between 2022 and 2035, carbon decoupling from total output value is projected to improve under both the Energy-Saving Scenario (ESS) and ERS, achieving strong decoupling, while the resource extraction shows limited decoupling effects often displaying an expansionary connection. This study aims to enhance the understanding and promote the advancement of green and low-carbon development within the MI in mineral-rich regions. Full article

As urbanization accelerates and climate change intensifies, the ecological integrity of urban riparian forests faces growing threats, underscoring the need for a systematic framework to guide their sustainable management. To address this gap, we developed a causal framework by applying text mining and sentence classification to 1001 abstracts from previous studies, structured within the DPSIR (Driver–Pressure–State–Impact–Response) model. The analysis identified six dominant thematic clusters—water quality, ecosystem services, basin and land use management, climate-related stressors, anthropogenic impacts, and greenhouse gas emissions—which reflect the multifaceted concerns surrounding urban riparian forest research. These themes were synthesized into a structured causal model that illustrates how urbanization, land use, and pollution contribute to ecological degradation, while also suggesting potential restoration pathways. To validate its applicability, the framework was applied to four major urban streams in Seoul, where indicator-based analysis and correlation mapping revealed meaningful linkages among urban drivers, biodiversity, air quality, and civic engagement. Ultimately, by integrating large-scale text mining with causal inference modeling, this study offers a transferable approach to support adaptive planning and evidence-based decision-making under the uncertainties posed by climate change. Full article

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

In the context of global climate governance and the green transition, digital infrastructure serves as a critical enabler of resource allocation in the digital economy, offering strategic value in tackling synergistic pollution and carbon reduction challenges. Using panel data from 280 prefecture-level cities, this study employs a multiperiod difference-in-differences (DID) approach, leveraging smart city pilot policies as a quasinatural experiment, to assess how digital infrastructure affects urban synergistic pollution-carbon mitigation (SPCM). The empirical results show that digital infrastructure increases the urban SPCM index by 1.5%, indicating statistically significant effects. Compared with energy and income effects, digital infrastructure can influence this synergistic effect through indirect channels such as the energy effect, economic agglomeration effect, and income effect, with the economic agglomeration effect accounting for a larger share of the total effect. Additionally, fixed-asset investment has a nonlinear moderating effect on this relationship, with diminishing marginal returns on emission reduction when investment exceeds a threshold. Heterogeneity tests reveal greater impacts in eastern, nonresource-based, and environmentally regulated cities. This study expands the theory of collaborative environmental governance from the perspective of new infrastructure, providing a theoretical foundation for establishing a long-term digital technology-driven mechanism for SPCM. Full article

The sustainable development of Chinese cities is of long-term significance. Multiple environmental regulatory instruments aim to promote the parallel advancement of environmental conservation and economic growth. This study examines three batches of low-carbon city pilot (LCCP) programs, employing eco-efficiency as the outcome variable. Using conventional difference-in-differences (DID) models, time-varying DID models, and period-by-period propensity score matching DID (PSM-DID) models with city and time fixed effects, we investigate the comprehensive impact of pilot policies on both economic and environmental performance. Eco-efficiency, measured through the Data Envelopment Analysis (DEA) model, exhibits a strong correlation with energy consumption patterns, as carbon emissions and air pollutants predominantly originate from non-clean energy utilization. The analysis reveals that LCCP policies significantly enhance eco-efficiency. These findings demonstrate robustness across placebo tests, endogeneity treatments, and alternative outcome variable specifications. The first and third LCCP batches significantly improve eco-efficiency, whereas the second batch demonstrates no statistically significant effect. Significant impacts emerge in regions where cities hold pilot status while provinces do not; conversely, regions where both cities and provinces participate in pilot programs show no significant effects. Finally, from an energy consumption perspective, policy recommendations are proposed to further enhance eco-efficiency through regulatory instruments. Full article

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article

This study uses financial data from 872 Chinese listed companies (2018–2022). It tests the triple dividend effect and threshold effect of China’s environmental protection tax (EPT) using high-dimensional fixed effects models and panel threshold models. We document that (1) EPT creates an environmental dividend for Chinese listed companies. It significantly reduces pollution emissions. A 1-unit tax increase reduces LnTPPE by 2.5%. (2) EPT creates a significant innovation dividend. It forces enterprises to improve the quality of authorized patents. A 1-unit tax increase raises patent technological complexity by 0.79%. (3) EPT creates an economic dividend. It significantly improves firm performance. A 1-unit tax increase raises relative corporate revenue by 38.1%. (4) EPT exerts significant threshold effects on micro-level triple dividend outcomes among Chinese listed companies. A heterogeneity analysis shows significant differences in threshold effects between non-heavily polluting and heavily polluting industries. This study confirms that China’s EPT generates a micro-level triple dividend effect alongside coexisting threshold effects for listed companies. This provides literature references for China to design and implement differentiated policies and offers a quantitative empirical case for implementing globally sustainable EPT strategies. Full article

Although particulate matter (PM) pollution from vehicles’ exhaust has decreased significantly over the years, the contribution from non-exhaust sources (brakes, tyres) has remained at the same levels. In the European Union (EU), Euro 7 regulation introduced PM limits for vehicles’ brake systems. Regenerative braking, i.e., recuperation of the deceleration kinetic and potential energy to the vehicle battery, is one of the strategies to reduce the brake emission levels and improve vehicle efficiency. According to the regulation, the shares of friction and regenerative braking can be determined with actual testing of the vehicle on a chassis dynamometer. In this study we tested the regenerative capabilities of a plug-in hybrid vehicle, both in the laboratory and on the road, under different protocols (including both smooth and aggressive braking) and covering a wide range of driving conditions (urban, rural, motorway) over 10,000 km of driving. Good agreement was obtained between laboratory and on-road tests, with the use of the friction brakes being on average 7% and 5.3%, respectively. However, at the same time it was demonstrated that the friction braking share can vary over a wide range (up to around 30%), depending on the driver’s behaviour. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

This study uses life cycle analysis (LCA) to evaluate the environmental impacts of co-firing bituminous coal with agricultural biomass waste, such as coconut and rice husks, emphasising circular economy principles. Seven experimental scenarios with different coal-to-biomass ratios were designed, ranging from pure coal to pure biomass. The results show that Scenario B (100% rice husk) achieved the best overall environmental performance, with the lowest global warming potential (300 kg CO₂ equivalent), eutrophication potential (4.742 kg PO₄ equivalent), and smog formation potential (0.012 kg C₂H₄ equivalent). Additionally, Scenario F (15% biomass mix) recorded the lowest acidification potential (57.39 kg SO₂ eq), indicating that even partial substitution can yield significant environmental benefits. In contrast, Scenario C (100% coal) exhibited the highest acidification (164.08 kg SO₂ eq) and eutrophication (8.82 kg PO₄ eq) potential. Overall, the results demonstrate that co-firing biomass waste significantly reduces pollutant emissions compared to burning coal alone. This study highlights the effectiveness of biomass waste co-firing in mitigating environmental impacts, promoting resource recovery, and supporting a sustainable energy transition within a circular economy framework. Full article