Search Results (117)

The Chinese red panda (Ailurus styani) is a rare and endangered animal in China; the increase in global temperature and the interference of human activities have caused irreversible effects on the suitable habitat of wild red pandas and threatened their survival. Therefore, it is necessary to carry out scientific research and protection for Chinese red pandas. In this study, the MaxEnt model was used to predict and analyze the suitable habitats of Chinese red pandas in the large and small Xiangling Mountains. The results showed that the main ecological factors affecting the suitable habitat distribution of Chinese red pandas in the Daxiangling Mountains are the average slope (45.6%, slope), the distance from the main road (24.2%, road), and the average temperature in the coldest quarter (11%, bio11). The main ecological factors affecting the suitable habitat distribution of Chinese red pandas in the Xiaoxiangling Mountains are bamboo distribution (67.4%, bamboo), annual temperature range (20.7%, bio7), and the average intensity of human activities (8.7%, Human Footprint). The predicted suitable habitat area of the Daxiangling Mountains is 123.835 km², and the predicted suitable habitat area of the Xiaoxiangling Mountains is 341.873 km². The predicted suitable habitat area of the Daxiangling Mountains accounts for 43.45% of the total mountain area, and the predicted suitable habitat area of the Xiaoxiangling Mountains accounts for 71.38%. The suitable habitat area of the Xiaoxiangling Mountains is nearly three times that of the Daxiangling Mountains, and the proportion of suitable habitat area of the Xiaoxiangling Mountains is much higher than that of the Daxiangling Mountains. The suitable habitat of Chinese red pandas in the Daxiangling Mountains is mainly distributed in the southeast, and the habitat is coherent but fragmented. The suitable habitat of Chinese red panda in Xiaoxiangling Mountains is mainly distributed in the east, and the habitat is more coherent. The results of this study can provide a scientific basis for the protection of the population and habitat of Chinese red pandas in Sichuan. Full article

The rice-wheat rotation is the main agricultural cropping system in Jiangsu Province, playing a vital role in ensuring food security and promoting economic development. However, current research on rice-wheat systems mainly focuses on in-situ controlled experiments at the point scale, with limited studies addressing carbon footprint (CF) and energy balance (EB) at the regional scale and long time series. Therefore, we analyzed the evolution patterns of the CF and EB of the rice-wheat system in Jiangsu Province from 1980 to 2022, as well as their influencing factors. The results showed that the sown area and total yield of rice and wheat exhibited an increasing–decreasing–increasing trend during 1980–2022, while the yield per unit area increased continuously. The CF of rice and wheat increased by 4172.27 kg CO₂ eq ha⁻¹ and 2729.18 kg CO₂ eq ha⁻¹, respectively, with the greenhouse gas emissions intensity (GHGI) showing a fluctuating upward trend. Furthermore, CH₄ emission, nitrogen (N) fertilizer, and irrigation were the main factors affecting the CF of rice, with proportions of 36%, 20.26%, and 17.34%, respectively. For wheat, N fertilizer, agricultural diesel, compound fertilizer, and total N₂O emission were the primary contributors, accounting for 42.39%, 22.54%, 13.65%, and 13.14%, respectively. Among energy balances, the net energy (NE) of rice exhibited an increasing and then fluctuating trend, while that of wheat remained relatively stable. The energy utilization efficiency (EUE), energy productivity (EPD), and energy profitability (EPF) of rice showed an increasing and then decreasing trend, while wheat decreased by 46.31%, 46.31%, and 60.62% during 43 years, respectively. Additionally, N fertilizer, agricultural diesel, and compound fertilizer accounted for 43.91–45.37%, 21.63–25.81%, and 12.46–20.37% of energy input for rice and wheat, respectively. Moreover, emission factors and energy coefficients may vary over time, which is an important consideration in the analysis of long-term time series. This study analyzes the ecological and environmental effects of the rice-wheat system in Jiangsu Province, which helps to promote the development of agriculture in a green, low-carbon, and high-efficiency direction. It also offers a theoretical basis for constructing a low-carbon sustainable agricultural production system. Full article

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

Italy supplies about one-seventh of the European Union’s total glass production, and the sector’s sizeable resource demands make it a linchpin of national industrial strategy. With growing environmental regulations and the push for resource efficiency, Material Flow Accounting has become essential for companies to stay compliant and advance sustainability. The investigation concentrates on Italy’s glass industry to clarify its material requirements, ecological footprint, and overall sustainability performance. STAN software v2, combined with an Economy-Wide Material Flow Accounting (EW-MFA) framework, models the national economy as a single integrated input–output system. By tracking each material stream from initial extraction to end-of-life, the analysis delivers a cradle-to-grave picture of the sector’s environmental impacts. During the 2021 production year, Italy’s glass makers drew on a total of 10.5 million tonnes (Mt) of material inputs, supplied 76% (7.9 Mt) from domestic quarries, and 24% (2.6 Mt) via imports. Outbound trade in finished glass removed 1.0 Mt, leaving 9.5 Mt recorded as Domestic Material Consumption (DMC). Within that balance, 6.6 Mt (63%) was locked into long-lived stock, whereas 2.9 Mt (28%) left the system as waste streams and airborne releases, including roughly 2.1 Mt of CO₂. At present, the post-consumer cult substitutes only one-third of the furnace batch, signalling considerable scope for improved circularity. When benchmarked against EU-27 aggregates for 2021, Italy registers a NAS/DMI ratio of 0.63 (EU median 0.55) and a DPO/DMI ratio of 0.28 (EU 0.31), indicating a higher share of material retained in stock and slightly lower waste generated per ton of input. A detailed analysis of glass production identifies critical stages, environmental challenges, and areas for improvement. Quantitative data on material use, waste generation, and recycling rates reveal the industry’s environmental footprint. The findings emphasise Economy-Wide Material Flow Accounting’s value in evaluating and improving sustainability efforts, offering insights for policymakers and industry leaders to drive resource efficiency and sustainable resource management. Results help scholars and policymakers in the analysis of the Italian glass industry context, supporting in the data gathering, while also in the use of this methodology for other sectors. Full article

Energy conversion and infrastructure facilities consist of large amounts of metal and have lifetimes of several decades. When recycling metals, the methods of allocation play a decisive role in evaluating how primary and secondary materials, as well as the products that are produced with them, are to be evaluated ecologically. So-called credits for recycling are the subject of a particularly controversial discussion. This article shows that the current practice of giving credits for long-lasting products leads to a significant distortion of the actual emissions. Using the examples of steel, aluminum, and copper, prospective LCA data is used to show how the carbon footprint actually behaves. When credits are applied, the time dependency of emissions must be taken into account; otherwise, burden shifting into the future occurs, which can hardly be considered sustainable. The increase compared to the conventional time-independent practice lies, depending on the metal, at 70 to 300%. It is recommended that the cutoff approach be used conservatively when allocating recycling cascades in order to optimize environmental impact and avoid greenwashing. Full article

The sustainable growth of finfish farming relies heavily on reducing the high ecological footprint of sourcing and producing fish feeds that accounts for almost 50% of the total ecological footprint of finfish farming. Sustainable alternatives to fishmeal often pose challenges due to the presence of antinutritional factors and nutrient imbalances that impair fish health and growth. Screening for alternative nutrient sources and adapting to global commodity fluctuations requires modern tools that can predict the physiological responses of fish early and reliably. The present study explores for the first time the potential of fish red blood cell (RBC) transcriptome as a minimally invasive biomarker of physiological responses in gilthead seabream (Sparus aurata) fed either a fishmeal-based (FM) or a plant-protein-based (PP) diet. Blood samples were collected at multiple time points (15, 20, and 30 days post-diet initiation) from genetically diverse full-sib families reared under commercial conditions, integrating transcriptomic analysis with long-term growth assessments. Differential gene expression analysis revealed significant dietary effects on oxidative phosphorylation, ribosomal capacity, and lipid metabolism pathways, highlighting metabolic plasticity and cellular adaptations to plant-based feeds. The downregulation of oxidative phosphorylation genes suggests a metabolic shift in response to altered nutrient composition, while ribosomal pathway modulation indicates potential constraints on protein synthesis. These transcriptomic shifts, conserved across two independent experiments, reinforce the utility of RBCs as a real-time indicator of fish physiological status, offering a tool for monitoring dietary impacts and optimizing feed formulations. Such insights are essential for advancing sustainable, nutritionally balanced aquaculture feeds that support fish welfare and productivity. The minimally invasive sample collection respects the 3Rs (Reduce, Refine, Replace) principle in animal experimentation and allows for frequent screening and generation of refined data. Full article

Within the framework of “dual carbon”, intending to enhance the use of green energies and minimize the emissions of carbon from energy systems, this study suggests a cost-effective low-carbon scheduling model that accounts for stepwise carbon trading for an integrated electricity, heat, gas, cooling, and hydrogen energy system. Firstly, given the clean and low-carbon attributes of hydrogen energy, a refined two-step operational framework for electricity-to-gas conversion is proposed. Building upon this foundation, a hydrogen fuel cell is integrated to formulate a multi-energy complementary coupling network. Second, a phased carbon trading approach is established to further explore the mechanism’s carbon footprint potential. And then, an environmentally conscious and economically viable power dispatch model is developed to minimize total operating costs while maintaining ecological sustainability. This objective optimization framework is effectively implemented and solved using the CPLEX solver. Through a comparative analysis involving multiple case studies, the findings demonstrate that integrating electric-hydrogen coupling with phased carbon trading effectively enhances wind and solar energy utilization rates. This approach concurrently reduces the system’s carbon emissions by 34.4% and lowers operating costs by 58.6%. Full article

Water resources are considered to be of paramount importance to the natural world on a global scale, being critical for the sustenance of ecosystems, the support of life, and the achievement of sustainable development. However, these resources are under threat from climate change, population growth, urbanization and pollution. This necessitates the development of robust and effective assessment methods to ensure their sustainable use. Although assessing the ecological footprint (EF) of urban water systems plays a critical role in advancing sustainable cities and managing water assets, existing research has largely overlooked the application of geospatial visualization techniques in evaluating resource allocation strategies within karst mountain watersheds, an oversight this study aims to correct through innovative methodological integration. This research establishes an evaluation framework for predicting water resource availability in Guizhou through the synergistic application of three methodologies: (1) the water-based ecological accounting framework (WEF), (2) ecosystem service thresholds defined by the water ecological carrying capacity of water resources (WECC) thresholds, and (3) composite sustainability metrics, all correlated with contemporary hydrological utilization profiles. Spatiotemporal patterns were quantified across the province’s nine administrative divisions during the 2013–2022 period through time-series analysis, with subsequent WEF projections for 2023–2027 generated via Long Short-Term Memory (LSTM) temporal forecasting techniques. Full article

This study incorporated ecological footprint (EF) consumption into a framework to assess ecological well-being. A model and implementation framework for characterizing regional net ecological well-being were then developed. Using the Guanzhong Plain Urban Agglomeration (GPUA) as a case study, land use data from 2000 to 2020 were utilized to calculate the ecosystem service value (ESV), representing the supply side of regional ecological functions. Simultaneously, the regional EF consumption was assessed as the demand side. Taking into account the level of regional economic development and the characteristics of people’s living, a regional net ecological well-being evaluation model was constructed to arrive at a deficit or surplus ecological situation. The results indicated that: (1) The overall ESV of the GPUA follows a trend of initial growth followed by a decline. Woodland, grassland, and farmland are the main contributors to the total ESV, with regulating and supporting services accounting for more than 80% of the total ecosystem value. (2) EF consumption in the GPUA shows a significant upward trend, increasing by over 70% on average. The level of ecological carrying capacity has slightly increased, with the biologically productive area that can support human activities expanding to 1909.49 million hectares. Additionally, the carrying capacity of the urban agglomeration cities has tended to stabilize since 2015. (3) Since 2010, anthropogenic consumption in the GPUA has continued to exceed the regional ecological capacity, resulting in an ecological well-being deficit. The average ecological well-being compensation per hectare in the urban agglomeration increased from 35.588 CNY to 187.110 CNY. This study offers a theoretical foundation for expanding the definition and research framework of regional ecological well-being by providing a more accurate assessment of regional ecological service supply and consumption at multiple scales. It is expected that this approach will help reduce the opportunity costs associated with ecological protection, while promoting a balanced approach to economic development and ecological preservation. Full article

Artificial Intelligence (AI) plays a dual role in the clean energy transition, acting both as a major energy consumer and as a driver of sustainability. While AI enhances renewable energy forecasting, optimizes smart grids, and improves energy storage efficiency, the rapid growth of AI-driven data centers has significantly increased global electricity demand. AI-related energy consumption is projected to double by 2026 and triple by 2030, accounting for approximately 1.3% of global electricity use. This study adopts a multidisciplinary approach, synthesizing engineering, business, and policy insights to evaluate AI’s energy footprint and contributions to sustainability. The findings reveal that AI-driven optimization enhances smart grid efficiency and forecasting accuracy; however, infrastructure limitations, regulatory gaps, and economic constraints hinder AI’s alignment with sustainability goals. The results are systematically structured across five key themes: key findings, impact on energy consumption, risks and challenges, potential solutions, and policies and regulations. Supported by thematic tables and an original infographic, this study provides a comprehensive analysis of AI’s evolving role. By integrating AI with global sustainability policies, stakeholders can leverage its potential to accelerate the clean energy transition while minimizing the ecological footprint. Full article

The aim of this study is to evaluate the environmental impact of solar energy by analyzing its emissions, resource consumption, and waste generation throughout its life cycle. As one of the most widely adopted energy sources, solar power offers substantial benefits in reducing greenhouse gas emissions; however, its broader environmental footprint requires careful examination. The production, operation, and disposal of solar panels contribute to pollution, water consumption, and hazardous waste accumulation, with an estimated 250,000 tons of solar waste reported in 2016 alone. Furthermore, solar power generation requires significant water resources, averaging 650 gallons per megawatt-hour of electricity. A key focus of this study is the emissions associated with solar technology, particularly during panel manufacturing and operation. Using HOMER Pro software, this research quantifies the emissions from Trina Solar photovoltaic (PV) panels (345 Wp), revealing an annual output of 49,259 kg of carbon dioxide, 214 kg of sulfur dioxide, and 104 kg of nitrogen dioxide. This Study obtained using HOMER Pro primarily account for operational emissions and do not include full lifecycle impacts such as raw material extraction, transportation, and disposal. These findings highlight the trade-offs between solar energy’s environmental advantages and its indirect ecological costs. While solar systems contribute to energy security and long-term economic savings, their environmental implications must be factored into energy planning and sustainability strategies. This study underscores the importance of developing greener manufacturing processes, improving recycling strategies, and optimizing solar farm operations to reduce emissions and resource depletion. By providing a comprehensive assessment of solar energy’s environmental impact, this research contributes valuable insights for policymakers, researchers, and industry leaders seeking to balance the benefits of solar power with sustainable environmental management. Full article

As an important engine of China’s development, the Yangtze River Economic Belt faces the dual contradiction of economic growth and ecological protection. Addressing the insufficient analysis of the spatiotemporal evolution and driving mechanisms of city-level carbon footprints, this study delves into the concept of carbon footprint from the perspective of ecological footprint theory and carbon cycle dynamics. Using ODIAC and NPP data, it systematically evaluates carbon footprints across 130 cities and examines their spatiotemporal evolution and driving factors using kernel density estimation and the Kaya-LMDI model. The results show (1) a significant growth trend in carbon footprint, with rapid expansion from 2000 to 2012, followed by fluctuating growth from 2012 to 2022; (2) a west-to-east “low–high” spatial pattern, where disparities have narrowed but absolute gaps continue to widen, leading to polarization; and (3) economic growth and urban expansion as the primary drivers of carbon footprint growth, while ecological land use pressure and carbon sequestration capacity played a major role in mitigation, with the impact of carbon sequestration foundations remaining limited. This study conducts precise regional carbon sink accounting and offers a new perspective on the quantitative analysis of carbon footprint drivers. The findings provide insights for low-carbon governance and sustainable urban development in the Yangtze River Economic Belt. Full article

Since 1 January 2024, French local authorities will be required to offer householders a means of recovering biowaste, either as a soil improver or as an energy source. Several criteria influence their choice: cost, availability of operators and equipment, social facilitation, etc. However, greenhouse gas (GHG) emissions are rarely taken into account in the decision-making process. This article compares the emissions of four biowaste recovery systems, differentiated by their process (composting or vermicomposting) and management type (community or industrial). It is based on the carbon footprint method defined by the French Agency for Ecological Transition (ADEME). The assumptions and emission factors come from two sources: a field survey of composting and vermicomposting companies and associations in the Lyon area and a review of the literature on GHG emissions from the decomposition of organic matter. The carbon footprint of the processes was determined by estimating the CO₂ equivalent per ton of composted biowaste. The results show that industrial composting emits the most carbon (CO₂). Depending on whether biogenic carbon is taken into account or not, the ranking of the other three processes changes. When biogenic CO₂ is taken into account, it is the process that has the greatest influence on the result; on the other hand, when biogenic CO₂ emissions are not taken into account, the type of management determines the ranking. These results are discussed in relation to the methodological limitations of the comparison, other biowaste management options and the reduction of biowaste-related emissions. For example, by studying the agricultural use of biowaste compost, the carbon balance could be refined by including the emissions avoided from the production of nitrogen fertiliser. However, environmental assessment is only one of a number of decision-making factors (social, economic, agricultural, etc.) in waste management. Full article

Taking six provinces and one city in the middle and lower reaches of the Yangtze River as the main research object, this study investigated the carbon footprint of agricultural production in the region and promoted the development of agricultural carbon reduction. This study used the internationally mainstream IPCC emission factor method to calculate the carbon footprint of agricultural production, and selected indicators such as rural population, crop planting area, rural per capita GDP, and urbanization rate to analyze the influencing factors of agricultural carbon footprint in various provinces in the middle and lower reaches of the Yangtze River based on an extensible STIRPAT model. Due to differences in agricultural production conditions, the carbon footprint per unit area and unit yield vary among provinces and cities in the middle and lower reaches of the Yangtze River. From the 15 year average, the carbon footprint per unit area is synchronized with the carbon footprint per unit yield, with Zhejiang Province having the highest (9830.48 kg (CO₂ eq)/hm², 0.65 kg (CO₂ eq)/kg), Hubei Province in the middle (5017.90 kg (CO₂ eq)/hm², 0.54 kg (CO₂ eq)/kg), and Jiangxi Province having the lowest (3446.181 kg (CO₂ eq)/hm², 0.46 kg (CO₂ eq)/kg). From the perspective of emission structure, the carbon footprint generated by agricultural resource inputs accounts for the largest proportion, with fertilizer and fuel use being the main contributors to emissions. In the analysis of influencing factors, the indicators that mainly promote the carbon footprint of agricultural production include the following: rural population (R), ratio of agricultural value added to GDP(Z), total sown area of crops (B), level of agricultural technology (total power of agricultural machinery) (J), and degree of agricultural mechanization (N). The indicators that mainly inhibit the carbon footprint of agricultural production include the per capita disposable income of rural residents (P), rural GDP per capita (G), and urbanization rate (C). Other indicators have a relatively weak impact on carbon footprint. Overall, optimizing agricultural resource input, improving mechanized productivity, and reasonably controlling fertilizers are important ways of reducing carbon emissions from agricultural production. In the middle and lower reaches of the Yangtze River, it is still necessary to formulate emission reduction measures tailored to different ecological environment characteristics to achieve sustainable agricultural development. Full article

Taking into account the approaches to ecology and social policy, the development of technologies for optimizing the combustion process for thermal power plants, one of the key sources of greenhouse gas emissions, is relevant. This article analyzes approaches that improve the combustion process efficiency in thermal power plants, as well as speed up the development of various operating modes. Particular attention is paid to the control of fuel composition and geometric parameters of a burner device. Optimal settings of these parameters can significantly impact the reduction in harmful emissions into the atmosphere, though finding such parameters is a labor-intensive process and requires the use of modern automation and data processing tools. Nowadays, the main methods to analyze and optimize various characteristics are machine learning methods based on artificial neural networks (ANNs), which are used in this work. These methods also demonstrate the efficiency in combination with the optimization method. Thus, the use of approaches based on the combustion process optimization can significantly improve the environmental footprint of thermal power plants, which meets modern environmental requirements. The obtained results show that the most significant effect on the $N{O}_X$ content has the mass flow rate change of primary air and fuel with a change in geometric parameters. The decrease in $N{O}_X$ concentration in comparison with the calculation results with basic values is about 15%. Full article