Search Results (216)

The equitable allocation of carbon emission responsibility is fundamental to advancing China’s industrial decarbonization, achieving its dual-carbon goals, and realizing regional sustainable development. However, prevailing interprovincial carbon accounting frameworks often neglect the coupled dynamics of economic benefits, energy flows, and ecological capacity, leading to systematic misattribution of industrial carbon footprint transfers. Here, we develop an integrated analytical framework combining multi-regional input–output (MRIO) modeling and net primary productivity (NPP) assessment to comprehensively quantify industrial carbon footprints and their transfers across 30 Chinese provinces. By embedding both the benefit principle (aligning responsibility with trade-generated economic gains) and the energy flow principle (accounting for interprovincial energy trade), we construct a dual-adjustment mechanism that rectifies spatial and sectoral imbalances in traditional accounting. Our results reveal pronounced east-to-west industrial carbon footprint transfers, with resource-rich provinces (e.g., Inner Mongolia, Xinjiang) disproportionately burdened by external consumption, impacting the balance of sustainable development in these regions. Implementing benefit and energy flow adjustments redistributes responsibility more fairly: high-benefit, energy-importing provinces (e.g., Shanghai, Jiangsu, Beijing) assume greater carbon obligations, while energy-exporting, resource-dependent regions see reduced responsibilities. This approach narrows the gap between production- and consumption-based accounting, offering a scientifically robust, policy-relevant pathway to balance regional development and environmental accountability. The proposed framework provides actionable insights for designing carbon compensation mechanisms and formulating equitable decarbonization policies in China and other economies facing similar regional disparities. Full article

Geothermal energy has been recognized as a promising renewable resource for sustainable power generation; however, the efficiency of conventional geothermal power plants has remained relatively low, and high investment costs have limited their competitiveness with other renewable technologies. In this context, the present study introduced an innovative geothermal electricity generation system aimed at enhancing energy efficiency, cost-effectiveness, and sustainability. Unlike traditional configurations, the system raised the geothermal source temperature passively by employing advanced heat transfer mechanisms, eliminating the need for additional energy input. Comprehensive energy, exergy, and exergoeconomic analyses were carried out, revealing a net power output of 43,210 kW and an energy efficiency of 30.03%, notably surpassing the conventional Kalina cycle’s typical 10.30–19.48% range. The system’s annual electricity generation was 11,138.53 MWh, with an initial investment of USD 3.04 million and a short payback period of 3.20 years. A comparative assessment confirmed its superior thermoeconomic performance. In addition to its technoeconomic advantages, the environmental performance of the proposed configuration was quantified. A streamlined life cycle assessment (LCA) was performed with a functional unit of 1 MWh of net electricity. The proposed system exhibited a carbon footprint of 20–60 kg CO₂ eq MWh⁻¹ (baseline: 45 kg CO₂ eq MWh⁻¹), corresponding to annual emissions of 0.22–0.67 kt CO₂ eq for the simulated output of 11,138.53 MWh. Compared with coal- and gas-fired plants of the same capacity, avoided emissions of approximately 8.6 kt and 5.0 kt CO₂ eq per year were achieved. The water footprint was determined as ≈0.10 m³ MWh⁻¹ (≈1114 m³ yr⁻¹), which was substantially lower than the values reported for fossil technologies. These findings confirmed that the proposed system offered a sustainable alternative to conventional geothermal and fossil-based electricity generation. Multi-objective optimization using NSGA-II was carried out to maximize energy and exergy efficiencies while minimizing total cost. Key parameters such as turbine inlet temperature (459–460 K) and ammonia concentration were tuned for performance stability. A sensitivity analysis identified the heat exchanger, the first condenser (Condenser 1), and two separators (Separator 1, Separator 2) as influential on both performance and cost. The exergoeconomic results indicated Separator 1, Separator 2, and the turbine as primary locations of exergy destruction. With an LCOE of 0.026 USD/kWh, the system emerged as a cost-effective and scalable solution for sustainable geothermal power production without auxiliary energy demand. Full article

Under the combined influence of global climate change and intensified human activities, ecosystem services (ESs) are undergoing substantial transformations. Identifying their nonlinear driving mechanisms is crucial for promoting regional sustainable development. Taking Anhui Province as a case study, this research evaluates the spatial patterns and temporal dynamics of six key ecosystem services from 2000 to 2020—namely, biodiversity maintenance (BM), carbon fixation (CF), crop production (CP), net primary productivity (NPP), soil retention (SR), and water yield (WY). The InVEST and CASA models were employed to quantify service values, and the XGBoost–SHAP framework was used to reveal the nonlinear response paths and threshold effects of dominant drivers. Results show a distinct “high in the south, low in the north” spatial gradient of ES across Anhui. Regulatory services such as BM, NPP, and WY are concentrated in the southern mountainous areas (high-value zones > 0.7), while CP is prominent in the northern and central agricultural zones (>0.8), indicating a clear spatial complementarity of service types. Over the two-decade period, areas with significant increases in NPP and CP accounted for 50% and 64%, respectively, suggesting notable achievements in ecological restoration and agricultural modernization. CF remained stable across 98.3% of the region, while SR and WY exhibited strong sensitivity to topography and precipitation. Temporal trend analysis indicated that NPP rose from 395.83 in 2000 to 537.59 in 2020; SR increased from 150.02 to 243.28; and CP rose from 203.18 to 283.78, reflecting an overall enhancement in ecosystem productivity and regulatory functions. Driver analysis identified precipitation (PRE) as the most influential factor for most services, while elevation (DEM) was particularly important for CF and NPP. Temperature (TEM) and potential evapotranspiration (PET) affected biomass formation and hydrothermal balance. SHAP analysis revealed key threshold effects, such as the peak positive contribution of PRE to NPP occurring near 1247 mm, and the optimal temperature for BM at approximately 15.5 °C. The human footprint index (HFI) exerted negative impacts on both BM and NPP, highlighting the suppressive effect of intensive anthropogenic disturbances on ecosystem functioning. Anhui’s ES exhibit a trend of multifunctional synergy, governed by the nonlinear coupling of climatic, hydrological, topographic, and anthropogenic drivers. This study provides both a modeling toolkit and quantitative evidence to support ecosystem restoration and service optimization in similar transitional regions. Full article

This study develops an ontology-based decision support framework to enhance sustainable polymer recycling within the circular economy. The framework, constructed in Protégé (OWL 2), systematically captures polymer categories with emphasis on polyethylene terephthalate (PET), polylactic acid (PLA), and rigid polyvinyl chloride (PVC) as well as recycling processes, waste classifications, and sustainability indicators such as carbon footprint. Semantic reasoning was implemented using the Semantic Web Rule Language (SWRL) and SPARQL Protocol and RDF Query Language (SPARQL) to infer optimal material flows and sustainable pathways. Validation through a UK industrial case study confirmed both the framework’s applicability and highlighted barriers to large-scale recycling, including performance gaps between virgin and recycled polymers. The comparative analysis showed carbon footprints of 2.8 kg CO₂/kg for virgin PET, 1.5 kg CO₂/kg for PLA, and 2.1 kg CO₂/kg for PVC, underscoring material-specific sustainability challenges. Validation through a UK industrial case study further highlighted additive complexity in PVC as a major barrier to large scale recycling. Bibliometric and thematic analyses conducted in this study revealed persistent gaps in sustainability metrics, lifecycle assessment, and semantic support for circular polymer systems. By integrating these insights, the proposed framework provides a scalable, data-driven tool for evaluating and optimising polymer lifecycles, supporting industry transitions toward resilient, circular, and net-zero material systems. Full article

Progressive climate change and the increasing concentration of carbon dioxide in the atmosphere represent one of the most serious challenges facing modern energy systems. At the same time, the global overproduction of plastics, particularly polyethylene terephthalate (PET), places a significant burden on the natural environment and waste management infrastructure. Electrochemical reactors offer a promising solution by enabling the simultaneous conversion of CO₂ and EG into valuable products such as carbon monoxide and glycolic acid, using electricity derived from renewable energy sources. Carbon monoxide can be further processed into high-energy synthetic fuels, such as propanol, while glycolic acid holds substantial importance in the pharmaceutical and plastics industries. An economic analysis was conducted to estimate the capital expenditures required for an electrochemical reactor and to assess the investment’s profitability based on the net present value (NPV) indicator. In addition, a Life Cycle Assessment (LCA) was carried out to evaluate the environmental impact of the proposed technology, with particular attention to its carbon footprint. The results indicate that the profitability of the system strongly depends on the market price and purity of glycolic acid, as well as on access to low-cost renewable electricity. The LCA confirms a significantly lower carbon footprint compared to conventional CO production, though further technological advancements are required for industrial deployment. Full article

Climate change is inducing new and increasing existing hazards that can cascade from one system or region to another affecting communities, ecosystems and various sectors of the economy. In 2022 Greece has incorporated the national climate law describing climate actions on mitigation and adaptation, introducing strategies and outlining key priorities and commitments of the country. Moreover, under article 20 of the national climate law, it is declared that it is mandatory for enterprises and public bodies to calculate their carbon footprint and publish a carbon footprint report in which mitigation actions and measures are also summarized. Reports collected within the first 2 years of the implementation of the law have been reviewed and data extracted are discussed in this work. A clear reduction of the total carbon footprint is evident in most sectors of Greek economy. Policy recommendations to enhance not only regulatory but also voluntary compliance and ensure progress towards the 2050 net-zero carbon emissions target are also outlined. Full article

This study aims to evaluate and compare the carbon emissions and reduction strategies of two different slope construction methods—concrete slope protection and ecological sprayed-soil slope protection—using a life-cycle assessment (LCA) approach. The research focuses on identifying key carbon emission sources throughout each stage of the construction, from material production to transportation, construction, and maintenance, with a particular emphasis on the ecological benefits of vegetation in reducing carbon footprints. Results indicate that the ecological slope protection scheme significantly outperforms the concrete scheme, reducing total carbon emissions by 667.21 tons. Furthermore, the ecological solution, due to its carbon sequestration capabilities, is projected to achieve carbon neutrality within 3.66 years after completion, offering a net carbon sequestration benefit of 2422.97 tons over its lifecycle. Optimization strategies across various stages—material production, transportation, construction, and maintenance—further reduce emissions by 56.8%, underscoring the potential for ecological slope protection to contribute to sustainable construction practices. This study not only provides valuable insights into low-carbon construction methods but also highlights the importance of integrating ecological and engineering technologies to meet global carbon reduction goals. Full article

Background/Objectives: The healthcare sector is a significant source of pollution, and it is widely acknowledged that changes are required to transition to more sustainable healthcare practice. Nurses represent more than half of the sector’s workforce and are uniquely positioned to enact change. However, expertise on environmental sustainability within the nursing field is a barrier despite the topic being positively embraced by students. Methods: This research employed a cross-sectional design using an anonymous online survey with convenience sampling from registered nursing students studying online to understand their attitudes towards environmentally sustainable healthcare. Data were collected between April 2023 and January 2024 with quantitative results analysed using descriptive statistics and qualitative results using thematic analysis. Results: Results show that registered nursing students are aware of the negative environmental impact of healthcare practice, realise the importance of working more sustainably and understand the value and role of education to facilitate meaningful change in the sector. However, they called for more educational content, specifically on carbon footprints, waste management, and resource use, paired with organisational leadership support and workplace training in healthcare settings. Conclusions: Adopting these recommendations endorsed by student nurses in practice could support nurses to reduce the environmental burden of the healthcare sector and contribute to both net zero and the United Nations Sustainable Development Goals. Full article

Addressing the carbon footprint in pig production is a fundamental technical basis for achieving carbon neutrality and peak carbon emissions. Only by systematically studying the carbon footprint can the goals of carbon neutrality and peak carbon emissions be effectively realized. This study aims to reduce the carbon footprint through optimized feeding strategies based on minimizing carbon emissions. To this end, this study conducted a full-lifecycle monitoring of the carbon footprint during pig growth from December 2024 to May 2025, optimizing feeding strategies using a real-time pig weight estimation model driven by deep learning to reduce resource consumption and the carbon footprint. We introduce EcoSegLite, a lightweight deep learning model designed for non-contact real-time pig weight estimation. By incorporating ShuffleNetV2, Linear Deformable Convolution (LDConv), and ACmix modules, it achieves high precision in resource-constrained environments with only 1.6 M parameters, attaining a 96.7% mAP50. Based on full-lifecycle weight monitoring of 63 pigs at the Pianguan farm from December 2024 to May 2025, the EcoSegLite model was integrated with a life cycle assessment (LCA) framework to optimize feeding management. This approach achieved a 7.8% reduction in feed intake, an 11.9% reduction in manure output, and a 5.1% reduction in carbon footprint. The resulting growth curves further validated the effectiveness of the optimized feeding strategy, while the reduction in feed and manure also potentially reduced water consumption and nitrogen runoff. This study offers a data-driven solution that enhances resource efficiency and reduces environmental impact, paving new pathways for precision agriculture and sustainable livestock production. Full article

Problem-based learning (PBL) has been recognized for enhancing student motivation and key competencies. However, its integration with emerging topics such as ESG (Environmental, Social, and Governance) in general education remains underexplored. This study implemented a PBL-based instructional design in a general education course titled “Organizational Greenhouse Gas Inventory and Net-Zero Transition,” integrating practical tasks and ESG case studies to enhance students’ sustainability literacy and core competencies. Pre- and post-course assessments were conducted using the University Career and Competency Assessment Network (UCAN) questionnaire, analyzed through paired sample t tests and Wilcoxon signed rank tests. Results showed significant improvements in the innovation and communication aspects, with upward trends observed in other domains. Students also demonstrated strong engagement and learning motivation through tasks such as carbon footprint estimation, data integration, and field-based assessments. The findings support the feasibility of embedding ESG and PBL frameworks in general education. Future course iterations will consider differentiated instructional design and the incorporation of qualitative methods to accommodate diverse student backgrounds and enhance learning outcomes, contributing to the advancement of sustainability education in higher education. Full article

Key concepts such as “carbon footprint”, “carbon neutral”, “carbon neutrality”, “low carbon”, and “net-zero emissions” have gained prominence in the context of climate change, a current issue that has become an urgent global challenge caused by anthropogenic activities, including agriculture. This bibliometric review analyzed the use of these concepts in mitigation strategies for the coffee sector, since coffee production significantly contributes to greenhouse gas (GHG) emissions, primarily due to land use change, fertilizer use, and processing methods, and therefore, sustainable approaches within the whole coffee value chain need to be implemented. A total of 105 documents from the Scopus database, covering publications from January 1988 to June 2023, were analyzed. Co-word analysis and co-occurrence mapping techniques, together with traditional bibliometric laws and historical evolution analysis using VOSviewer and Bibliometrix, were applied. The evolution of research over time revealed that the first concept introduced for documenting the reduction in greenhouse gas (GHG) emissions was “low carbon emissions” in 1909, but it was not until 2008 that the first document was published establishing a link between “low carbon emissions” and “coffee”. In 2015, two more concepts, “carbon neutral” and “carbon neutrality”, documented since 1968 and 1995, respectively, were used in articles related to coffee. So far, the most relevant concept in quantifying GHG emissions in the context of coffee production activities has been “carbon footprint”. When it comes to new documents linking key concepts to coffee, between 2015 and 2018, there was an average of six documents per year. Since 2019, the average has remained at 15, highlighting the need to continue documenting climate change mitigation strategies in the coffee sector. Practical application of our findings for coffee sustainability programs must include the adoption of on-farm sustainable agricultural practices that span the entire value chain. In conclusion, this study underscores the importance of concepts such as “carbon footprint” and “carbon neutrality” as key pillars in the development of effective climate change mitigation strategies in the coffee sector and the significance of their integration into future research and global policies with practical applications, with far-reaching implications for sustainable agriculture in the near future. Full article

To elucidate the food–energy–water (FEW) nexus, in this paper, a food–energy–water–carbon (FEWC) measurement method is established, and the evolutionary mechanisms within the nexus are determined to optimize crop production systems (CPSs). A quantitative assessment of the trade-offs and synergies among the constituent sub-nexuses is presented. This assessment is achieved through carbon footprint analysis of CPSs. In addition to examining FEW resource interactions, we employ the logarithmic mean divisia index methodology—a tool well-suited for practical energy decomposition—to explore the nexus interrelationships. This research further accounts for anthropogenic inputs in CPSs, specifically using blue water and energy consumption as key indicators for characterizing water and energy dynamics, respectively. Five crops are selected for CPS carbon emissions analysis to inform cropping structure optimization. The results show that during 2000–2022, greenhouse gas (GHG) emissions from China’s CPSs exhibited significant fluctuations characterized by a concentrated–dispersed–concentrated distribution pattern: the food system’s carbon footprint decreased notably, the food–energy (FE) system’s impact increased substantially, and the food–water (FW) system’s footprint fluctuated before decreasing. The spatial diversity in the FE system’s provincial carbon footprint increased over time, while the FW nexus exhibited fluctuating yet significant efficiency gains, indicating movement toward more balanced spatial distribution along the Hu Huanyong Line and Botai Line. The net effect of the FEW nexus interactions on GHG emissions exhibited a slight mitigating influence. Full article

Developing a more productive, resource-efficient, and climate-smart rubber agroforestry model is essential for the sustainable growth of natural rubber cultivation. In this study, we evaluated whether a double-row rubber plantation intercropped with the medicinal crop Ficus hirta Vahl. (DR-F) could achieve this goal, using a single-row rubber plantation (SR) as the control. We assessed the feasibility of the DR-F system based on productivity, solar utilization efficiency (SUE), partial factor productivity of applied nitrogen (PFPN), carbon efficiency (CE), net ecosystem carbon balance (NECB), and carbon footprint (CF). No significant difference was observed in rubber tree biomass between the DR-F (10.49 t·ha⁻¹) and SR (8.49 t·ha⁻¹) systems. However, the DR-F system exhibited significantly higher total biomass productivity (23.34 t·ha⁻¹) than the SR systems due to the substantial contribution from intercropped Ficus hirta Vahl., which yielded 12.84 t·ha⁻¹(p < 0.05). The root fresh weight yield of Ficus hirta Vahl. reached 17.55 t·ha⁻¹, generating an additional profit of 20,417 CNY ha⁻¹. The DR-F system also exhibited higher solar radiation interception and greater availability of soil nutrients. Notably, the roots of rubber trees and Ficus hirta Vahl. did not overlap at a 4 m distance from the rubber trees. The DR-F system achieved higher SUE (0.64%), PFPN (51.40 kg·kg⁻¹ N), and CE (6.93 kg·kg⁻¹ C) than the SR system, with the SUE and PFPN differences being statistically significant (p < 0.05). Although the NECB remained unaffected, the DR-F system demonstrated significantly higher productivity and a substantially lower CF (0.33 kg CO₂·kg⁻¹, a 56% reduction; p < 0.05). In conclusion, the DR-F system represents a more sustainable and beneficial agroforestry approach, offering improved productivity, greater resource use efficiency, and reduced environmental impact. Full article

Accurate and scalable estimation of forest production is essential for quantifying carbon sequestration, forecasting timber yields, and guiding climate change mitigation strategies. While prior studies established a positive linkage between net primary production (NPP) and canopy structural complexity (CSC) metrics derived from terrestrial LiDAR, the spatial coverage of ground-based surveys is limited. Airborne laser scanning (ALS) could offer a rapid and spatially extensive alternative to terrestrial scanning, but the predictive capacity of ALS-derived CSC metrics for estimating forest production remains insufficiently explored. To address this gap, we derived a suite of three-dimensional (3D) CSC metrics from small-footprint, high-density ALS data collected by the National Ecological Observatory Network’s Airborne Observation Platform. We evaluated relationships between CSC metrics and the NPP of plots nested within seven deciduous and evergreen temperate forests. Optimal metric combinations for predicting NPP within and across forest types were identified using partial least squares regression coupled with recursive feature elimination. ALS-derived CSC metrics explained 77% (RMSE = 11%) and 76% (RMSE = 13%) of the variance in deciduous and evergreen forest plot NPP, respectively. Our findings demonstrate that 3D CSC metrics derived from high-density ALS are robust predictors of plot-level NPP, offering performance comparable to terrestrial scanners while enabling greater scalability and more efficient data acquisition. Full article

Sustainable highway infrastructure maintenance critically depends on precise lane line detection, yet conventional inspection approaches remain resource-depleting, carbon-intensive, and hazardous to personnel. To mitigate these constraints and address the low accuracy and high parameterization of existing models, this study utilizes unmanned aerial vehicle (UAV) imagery and proposes a UAV-based highway lane line segmentation method using an improved DeepLabV3+ model that resolves multi-scale lane line segmentation challenges in UAV imagery. MobileNetV2 is used as the backbone network to significantly reduce the number of model parameters. The Squeeze-and-Excitation (SE) attention mechanism is integrated to enhance feature extraction capabilities, particularly at lane line edges. A Feature Pyramid Network (FPN) is incorporated to improve multi-scale lane line feature extraction. We introduce a novel Waterfall Atrous Spatial Pyramid Pooling (WASPP) module, utilizing cascaded atrous convolutions with strategic dilation rate adjustments to progressively expand the receptive field and aggregate contextual information across scales. The improved model outperforms the original DeepLabV3+ by 5.04% mIoU (85.30% vs. 80.26%) and 3.35% F1-Score (91.74% vs. 88.39%) while cutting parameters by 85% (8.03 M vs. 54.8 M) and reducing training time by 2 h 50 min, thereby enhancing the model’s accuracy in lane line segmentation, reducing the number of parameters, and lowering the carbon footprint. Full article