Search Results (645)

Given the critical importance of timely forest fire detection to mitigate environmental and socio-economic consequences, this research aims to achieve high detection accuracy while maintaining real-time operational efficiency, with a particular focus on minimizing computational complexity. We propose a novel framework that systematically integrates normalization, feature selection, adaptive oversampling, and classifier optimization to enhance detection performance while minimizing computational overhead. The evaluation is conducted using three distinct Canadian forest fire datasets: Alberta Forest Fire (AFF), British Columbia Forest Fire (BCFF), and Saskatchewan Forest Fire (SFF). Initial classifier benchmarking identified the best-performing tree-based model, followed by normalization and feature selection optimization. Next, four oversampling methods were evaluated to address class imbalance. An ablation study quantified the contribution of each module to overall performance. Our targeted, stepwise strategy eliminated the need for exhaustive model searches, reducing computational cost by 97.75% without compromising accuracy. Experimental results demonstrate substantial improvements in F1-score, AFF (from 69.12% to 82.75%), BCFF (61.95% to 77.91%), and SFF (90.03% to 96.18%) alongside notable reductions in False Negative Rates compared to baseline models. Full article

Rapid socio-economic development has made energy application and environmental issues increasingly prominent. Hydrogen energy, clean, eco-friendly, and highly synergistic with renewable energy, has become a global research focus. This study, using the EnergyPLAN model that includes the electricity, transportation, and industrial sectors, takes Jinan City as the research object and explores how hydrogen penetration changes affect the decarbonization path of the urban integrated energy system under four scenarios. It evaluates the four hydrogen scenarios with the entropy weight method and technique, placing them in an order of preference according to their similarity to the ideal solution, considering comprehensive indicators like cost, carbon emissions, and sustainability. Results show the China Hydrogen Alliance potential scenario has better CO₂ emission reduction potential and unit emission reduction cost, reducing them by 7.98% and 29.39%, respectively. In a comprehensive evaluation, it ranks first with a score of 0.5961, meaning it is closest to the ideal scenario when cost, environmental, and sustainability indicators are comprehensively considered. The Climate Response Pioneer scenario follows with 0.4039, indicating that higher hydrogen penetration in terminal energy is not necessarily the most ideal solution. Instead, appropriate hydrogen penetration scenarios should be selected based on the actual situation of different energy systems. Full article

This study addresses this gap by proposing a multilevel fuzzy evaluation model combined with an analytic hierarchy process (AHP) to quantify the greenness of furniture products across their entire lifecycle. Focusing on an office desk as a case study, we developed an indicator system encompassing environmental attributes, resource efficiency, energy consumption, economic costs, and quality performance. Weighting results revealed that environmental attributes (27.2%) and resource efficiency (27.2%) dominated the greenness evaluation, with material recycling rate (33.5%) and solid waste pollution (24.3%) as critical sub-indicators. The prototype achieved a moderate greenness score of 70.38/100, highlighting optimization potential in renewable material adoption (10% current rate) and modular design for disassembly. Mechanically recycled materials could reduce lifecycle emissions by 18–25% in key categories. The model demonstrates scalability for diverse furniture types and informs policy-making by prioritizing high-impact areas such as toxic material reduction and energy-efficient manufacturing, thus amplifying its global and interdisciplinary multiplier effects. Full article

This study examines the impact of environmental, social, and governance (ESG) practices on green innovation and financial performance among 174 publicly listed firms across ASEAN countries over the period from 2019 to 2023. Utilizing an unbalanced panel dataset of firms from key ASEAN economies, the analysis employs panel regression techniques. Green innovation performance is measured through innovation disclosures related to environmental technologies, while financial success is assessed via return on assets (ROA) and Tobin’s Q. The findings reveal that environmental and governance disclosure scores positively influence green innovation, whereas social scores exert a more immediate impact on financial performance. Moreover, green innovation is found to partially mediate the relationship between overall ESG practices and long-term market valuation. These results highlight the strategic role of ESG transparency in enhancing innovation-driven competitiveness, responsible business conduct, and sustainable employment across Southeast Asian markets. Implications are discussed for corporate managers, policymakers, and socially responsible investors. The study reinforces the case for ESG-aligned strategy as a pathway to both innovation, inclusive economic growth, and long-term competitiveness in ASEAN markets. Full article

The aim of this comprehensive review is to synthesize current knowledge on bovine respiratory disease (BRD), enhance diagnostic strategies, and support effective prevention and management practises. BRD remains a leading cause of morbidity and mortality in cattle, driven by a complex interplay of viral and bacterial pathogens, host factors, environmental stressors, and management conditions. Its prevalence (2.1% to 20.2%) varies across geographical regions, age groups, and diagnostic methods. BRD leads to significant economic losses through direct impacts such as mortality, reduced growth rates, and lighter carcass weights, as well as indirect costs like market restrictions and long-term productivity declines. Diagnosing BRD is challenging due to its non-specific clinical signs and frequent subclinical presentations. Traditional diagnostic tools like clinical respiratory scoring (CRS) systems provide structure but suffer from low sensitivity and subjectivity. Behavioural monitoring shows promise by detecting early changes in feeding, movement, and social behaviours. Thoracic auscultation is widely used but limited in accuracy. Thoracic ultrasonography (TUS) stands out as a more sensitive method for detecting subclinical disease and correlating with growth outcomes. Combining CRS with TUS enhances early and accurate detection. Advancing diagnostic approaches is critical for improving animal health and minimizing economic losses in cattle production systems. Full article

Producing Class A biosolids is a beneficial way to reuse wastewater treatment solids, but most conventional processes are energy-intensive and expensive. There is growing interest in the use of low-cost, low-tech (LCLT) Class A biosolids treatment processes, especially at small water resource recovery facilities (WRRFs). This study used a holistic sustainability assessment to examine the environmental, economic, and social sustainability of conventional and LCLT processes at small WRRFs. The technologies studied were Direct Heat Drying, Composting, Lagoon Storage, Air Drying, and Temperature-Phased Anaerobic Digestion (TPAD). Environmental impacts were determined by conducting life-cycle assessments for all technologies, which is described in detail in prior published work. Economic impacts were quantified with a life-cycle cost assessment approach over a 25-year time horizon. Potential social impacts of each process were assessed by investigating case studies and surveys of social response to biosolids and estimating a relative impact score in a number of categories reported to be important to stakeholders in this technical domain. Impacts were normalized and compared to assess the best processes under a range of weighting scenarios. TPAD and Air Drying were the most sustainable processes when all domains were weighted equally. TPAD was projected to have low environmental and social impacts, which made up for its relatively high lifetime cost. Air Drying was the least expensive process in our analysis and had a modest environmental footprint, but there is potential for higher social impacts if the process is not sited and maintained properly. Because different communities are likely to prioritize or weight environmental, economic, and social impacts differently, a three-component mixing diagram was used to illustrate that Air Drying (economic), TPAD (environmental), or Direct Heat Drying (social) could become the preferred biosolids treatment process depending on which of the three sustainability domains was prioritized in the analysis. Full article

The high sedimentation rate of Rawapening Lake is both an environmental challenge and a potential resource. Seedlings currently rely on single-use plastic polybags, which contribute significantly to plastic waste. The use of mineral soil as a growing medium can accelerate natural resource depletion. This study aims to evaluate the feasibility and sustainability of utilizing lake sediment as an alternative seedling media through soil block technology. An integrated Life Cycle Assessment was conducted to quantify the environmental impacts, and Multidimensional Scaling was applied to assess sustainability across environmental, technological, economic, social, and institutional dimensions. Field data from ten seedling producers using soil blocks and ten using polybags were analyzed. The results showed that soil block media reduced Global Warming Potential by 48% compared to polybags, increased phosphorus and organic matter content, and was more financially efficient, with an increase in productivity of 90.24% and a revenue cost ratio of 24.56%. Sustainability analysis classified the innovation as moderately sustainable, with the highest scores in the environmental and technological dimensions. Institutional support was identified as a limiting factor. These findings suggest that sediment-based soil block media are a viable, lower-impact alternative for seedling production, although scaling up will require policy and institutional support. Full article

Microalgae-based carbon sequestration is promising for implementing carbon neutrality and reducing greenhouse gas emissions. However, as the technology remains in its early developmental stages, it presents a range of risks that may deter potential investors. To address these risks, this study proposes a group-based decision-making framework for the risk evaluation of microalgae carbon sequestration projects. Fifteen risk indicators are identified and categorized into four groups, including economic, technical, market, and environmental. To handle uncertainty and vagueness in the assessment, the framework uses trapezoidal fuzzy numbers and hesitant fuzzy linguistic sets to evaluate benchmark values. An expert credibility model is developed to assign weights to expert opinions by combining the subjective RANCOM method and the objective centroid method, both adapted for a fuzzy linguistic environment. A generalized aggregation operator is then used to combine expert evaluations. This operator integrates weighted and ordered averaging techniques and converts probabilistic linguistic terms into trapezoidal fuzzy numbers. The final risk level is determined using a fuzzy comprehensive evaluation method. The results indicate a medium-high level of risk, with a similarity score of 0.960. This suggests that while microalgae carbon sequestration holds great promise, effective planning and risk management are essential. For project managers and investors, this proposed framework helps quantify risk. It provides practical guidance for improving decision-making and strengthening project management. Full article

The growth of urban areas and climate change affect the performance of water management, increasing the rate of flooding and decreasing the quality of available water. To address this issue, the sustainable urban drainage systems (SUDs) and conventional urban drainage systems (UDIs) must be promoted. In both systems, grey infrastructure plays an important role, in the form of reinforced concrete tanks, filters, and water treatment plants. Nowadays, the use of reinforced concrete is a major contributor of the environmental impact of human activities environmental impacts. This study aims to assess the potential of nanoparticle-based concrete to mitigate the environmental impacts of water management facilities. To achieve this target, a comparative Life Cycle Assessment (LCA) analysis was performed on a multi walled carbon nanotubes (MWCNTs) based concrete, and a conventional one. To evaluate the corresponding benefits, a Functional Unit has been defined representing a frequently used element in water management facilities. The conducted review found no similar research. It is noted that the functional units used in published studies on nanoproducts are usually defined for the production of mass units. This study, found that using MWCNT-based concrete reduced the weight of the steel reinforcement by 47%. This reduction in steel outweighs the environmental impacts corresponding to used MWCNTs. The impact scores obtained are significantly lower for the MWCNT-based concrete. Therefore, the use of this material is recommended in Water management facilities, only on an environmental basis. Further investigation is recommended into the economic viability of this use. 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

In this study, we examine the impact of mergers and acquisitions (M&As) on firm environmental performance, aiming to address the gap in research and guide firms, investors, and policymakers toward more environmentally conscious decision-making in M&A. Using panel data from Chinese A-share listed firms (2008–2022), we estimate a two-way fixed effect model. The Propensity Score Matching and the instrumental variable method address potential endogeneity concerns, and robustness checks validate the findings. We found that M&As have a significantly positive effect on firm environmental performance, with heterogeneous impacts across regions, industries, and M&A types. The environmental benefits are most pronounced in heavily polluting industries and hybrid M&A deals. Eastern China shows more modest improvements. The results of mechanism tests revealed that M&As enhance environmental performance primarily by boosting total factor productivity and fostering innovation. This study offers a novel perspective by linking M&A activities to environmental sustainability, enriching the literature on both M&As and corporate environmental performance. We show that even conventional M&A deals (not sustainability-focused) can improve environmental performance through operational synergies. Expanding beyond polluting industries, we reveal how sector characteristics shape M&A’s environmental impacts. We identify practical mechanisms through which standard M&A activities can advance sustainability goals, helping firms balance economic and environmental objectives. It provides empirical evidence from China, an emerging market with distinct institutional and regulatory contexts. The findings offer guidance for firms engaging in M&A to strategically improve sustainability performance. Policymakers can leverage these insights to design incentives for M&A in pollution-intensive industries, aligning economic growth with environmental goals. By demonstrating that M&As can enhance environmental outcomes, this study supports the potential for market-driven mechanisms to contribute to broader societal sustainability objectives, such as reduced industrial pollution and greener production practices. Full article

This study employs a mixed method. In-depth interviews and observational studies are among the data collection approaches used in qualitative research. The quantitative method measures the weight of respondents’ answers to the distributed questionnaire. The questionnaire, containing 82 items, was distributed to 202 tourists to collect their perceptions based on the 4A tourist components. The results indicate that tourists’ perceptions of attractions, accessibility, and ancillary services are generally positive. In contrast, perceptions of amenity services are less favorable. Using the scores from IFAS, EFAS, and the I-E matrix, the total weighted scores for IFAS and EFAS are 2.68 and 2.83, respectively. The appropriate strategy for BTV is one of aggressive growth in a position of strengths and opportunities. The study highlights key techniques, including the application of information technology in service and promotion, the strengthening of community and government roles, the development of infrastructure and facilities, the utilization of external resources, sustainable innovation, and the encouragement of local governments to issue regulations for halal tourism villages. By identifying drivers and barriers from an economic, environmental, social, and cultural perspective, the SWOT analysis results help design strategies that can make positive contributions to the development of sustainable, community-based halal tourism and digital information in the future. Full article

This study examines the complex relationship between environmental, social, and governance (ESG) and financial performance in the automotive industry, with a particular focus on how financial constraints shape this relationship. Using a global data set for the period 2008 to 2023 and employing a range of panel data techniques, including those addressing endogeneity concerns, we find that higher ESG scores positively affect financial performance. Specifically, a one-point rise in ESG score corresponds to an estimated 1–1.7% increase in the market-to-book ratio, with the effect reaching approximately 1.6% for firms facing financial constraints. These findings highlight the economic significance of ESG engagement, particularly for resource-constrained companies. The novelty of this study is that it focuses on the automotive sector, an industry with limited ESG-specific research, and that it makes a theoretical contribution by linking ESG performance outcomes to financial constraints, an angle largely overlooked in prior research. The findings offer critical policy insights, emphasizing the strategic importance of ESG initiatives for value creation under varying financial conditions. Full article

Efficient plastic waste end-of-life management is a serious worldwide environmental issue motivated by growing waste production and negative effects of wrongful disposal. This study presents a comparative overview of plastic waste management regimes within the European Union (EU), the United States of America (USA), and Romania, ranked with circular economy goals. By using the United States Environmental Protection Agency (US EPA) Waste Reduction Model (WARM), version 16, the study provides a quantified score to greenhouse gas (GHG) emissions within three large options of management: recycling, energy recovery through combustion, and landfilling. The model setup utilizes region-specific information on legislation, base technology, and recycling efficiency. The outcomes show that recycling always entails net GHG emissions reductions, i.e., −4.49 kg CO_2e/capita/year for EU plastic waste and −20 kg CO_2e/capita/year for USA plastic waste. Combustion and landfilling have positive net emissions from 1.76 to 14.24 kg CO_2e/capita/year. Economic indicators derived from the model also show significant variation: salaries for PET management amounted to USD 2.87 billion in the EU and USD 377 million in the USA, and tax collection was USD 506 million and USD 2.01 billion, respectively. The conclusions highlight the wider environmental and socioeconomic benefits of recycling and reinforce its status as a cornerstone of circular-economy sustainable plastic waste management and a strategic element of national development agendas, with special reference to Romania’s national agenda. Full article

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article