Pub Date : 2025-12-31DOI: 10.1016/j.eiar.2025.108317
Sijia Lin , Yuan Xu
Offshore wind has become a central pillar in the global energy transition as deployment expands and technology advances. This study develops a novel review and meta-analysis framework based on 45 learning curves to bridge divides between econometric and engineering perspectives and clarify how component characteristics and contextual factors shape offshore wind cost trajectories. Component-based learning curves recognize technological maturity differences between above-water and under-water components and their relatedness to onshore wind knowledge, with respective cost shares of 40 % and 60 % generating learning-by-doing variations of comparable magnitude. The learning-by-doing rates estimated for turbine manufacturing, capacity installation, and electricity generation at 7 %, 9 %, and 13 % indicate intensified cost reductions when emerging components are assessed jointly. Multi-factor learning curves capture contextual influences beyond cumulative outputs and show that learning spillovers and R&D investments drive cost reductions, whereas project expansions into further-offshore and deeper seas slow progress. By examining how model specifications and variable inclusions affect learning-by-doing rate estimates, fixed-effects and random-effects meta-regressions yield robust findings: One-factor learning curves overestimate learning-by-doing rates by 5.67 % relative to multi-factor models; installation-cost metrics derive learning-by-doing rates about 48 % lower than LCOE-based estimates; technological maturation decreases them by 2.2 % over time, indexed by the midpoint of the learning curve's temporal horizon; and industry-wide learning spillovers increase them by 6.83 %. These findings strengthen empirical foundations and provide practical guidance for future offshore wind cost research by underscoring the importance of evolving component cost structures, cross-industry technological relatedness, contextual interdependence, and broader policy and socio-economic implications of sustained cost reductions.
{"title":"Decoding the cost reduction of offshore wind technology through learning curves: A meta analysis","authors":"Sijia Lin , Yuan Xu","doi":"10.1016/j.eiar.2025.108317","DOIUrl":"10.1016/j.eiar.2025.108317","url":null,"abstract":"<div><div>Offshore wind has become a central pillar in the global energy transition as deployment expands and technology advances. This study develops a novel review and meta-analysis framework based on 45 learning curves to bridge divides between econometric and engineering perspectives and clarify how component characteristics and contextual factors shape offshore wind cost trajectories. Component-based learning curves recognize technological maturity differences between above-water and under-water components and their relatedness to onshore wind knowledge, with respective cost shares of 40 % and 60 % generating learning-by-doing variations of comparable magnitude. The learning-by-doing rates estimated for turbine manufacturing, capacity installation, and electricity generation at 7 %, 9 %, and 13 % indicate intensified cost reductions when emerging components are assessed jointly. Multi-factor learning curves capture contextual influences beyond cumulative outputs and show that learning spillovers and R&D investments drive cost reductions, whereas project expansions into further-offshore and deeper seas slow progress. By examining how model specifications and variable inclusions affect learning-by-doing rate estimates, fixed-effects and random-effects meta-regressions yield robust findings: One-factor learning curves overestimate learning-by-doing rates by 5.67 % relative to multi-factor models; installation-cost metrics derive learning-by-doing rates about 48 % lower than LCOE-based estimates; technological maturation decreases them by 2.2 % over time, indexed by the midpoint of the learning curve's temporal horizon; and industry-wide learning spillovers increase them by 6.83 %. These findings strengthen empirical foundations and provide practical guidance for future offshore wind cost research by underscoring the importance of evolving component cost structures, cross-industry technological relatedness, contextual interdependence, and broader policy and socio-economic implications of sustained cost reductions.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108317"},"PeriodicalIF":11.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.eiar.2025.108321
Roberta Stefanini, Giuseppe Vignali
The increasing use of plastic packaging has raised environmental concerns for their disposal. A barrier to recyclability is the popularity of multilayer solutions, consisting of different polymer films, with high-performance barrier, but with almost no material separability. Since circular economy calls for the design for recycling, a strategy is the reduction of structural complexity, favouring single-material configurations that can be processed in mechanical recycling streams.
In this context, this work aims at quantifying the environmental benefits potentially associated to the substitution of multi-material food packaging with recyclable ones. Three types of products have been selected as case studies: coffee (1 kg), dried fruit (200 g), cheese (200 g). Through Life Cycle Assessments (LCA), their conventional packaging configuration composed of polyethene terephthalate (PET), polyethylene (PE), aluminium, in complex multilayers, were compared with alternatives based primarily on polypropylene (PP) or polyethylene derivatives. Primary data on packaging composition, production process, transports, auxiliary materials were collected, supplemented by secondary data from Ecoinvent database. Attention was paid to end of lives, modelled using national consortium reports and RecyClass European tool.
Results shows that material choice and production influence packaging environmental impacts, with monomaterials generally performing better in gas emissions and eutrophication, while in resource- and water-related impacts show no clear advantage. End-of-life management is crucial, as proper recycling of monomaterials can significantly enhance sustainability, highlighting the need for consumer awareness and careful evaluation of trade-offs by manufactures. Since results depend on context and material, requiring case-by-case evaluation, future research need to expand products coverage.
{"title":"Quantifying environmental benefits of monomaterial transition in flexible packaging applications","authors":"Roberta Stefanini, Giuseppe Vignali","doi":"10.1016/j.eiar.2025.108321","DOIUrl":"10.1016/j.eiar.2025.108321","url":null,"abstract":"<div><div>The increasing use of plastic packaging has raised environmental concerns for their disposal. A barrier to recyclability is the popularity of multilayer solutions, consisting of different polymer films, with high-performance barrier, but with almost no material separability. Since circular economy calls for the design for recycling, a strategy is the reduction of structural complexity, favouring single-material configurations that can be processed in mechanical recycling streams.</div><div>In this context, this work aims at quantifying the environmental benefits potentially associated to the substitution of multi-material food packaging with recyclable ones. Three types of products have been selected as case studies: coffee (1 kg), dried fruit (200 g), cheese (200 g). Through Life Cycle Assessments (LCA), their conventional packaging configuration composed of polyethene terephthalate (PET), polyethylene (PE), aluminium, in complex multilayers, were compared with alternatives based primarily on polypropylene (PP) or polyethylene derivatives. Primary data on packaging composition, production process, transports, auxiliary materials were collected, supplemented by secondary data from Ecoinvent database. Attention was paid to end of lives, modelled using national consortium reports and RecyClass European tool.</div><div>Results shows that material choice and production influence packaging environmental impacts, with monomaterials generally performing better in gas emissions and eutrophication, while in resource- and water-related impacts show no clear advantage. End-of-life management is crucial, as proper recycling of monomaterials can significantly enhance sustainability, highlighting the need for consumer awareness and careful evaluation of trade-offs by manufactures. Since results depend on context and material, requiring case-by-case evaluation, future research need to expand products coverage.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108321"},"PeriodicalIF":11.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-27DOI: 10.1016/j.eiar.2025.108318
M.J. Mohammad Nasir , Mayank Suman , P. Ravi Prakash
The building sector consumes vast resources and energy, contributing significantly to environmental degradation. Addressing these challenges requires Building Sustainability Assessment (BSA) methods such as Life Cycle Assessment (LCA), Life Cycle Cost Analysis (LCCA), and Green Building Rating Systems (GBRSs). This paper presents a BIM-BSA framework in the Indian context, integrating Building Information Modeling (BIM) with LCA, LCCA, and the GRIHA-2019 and IGBC rating systems. Dynamo scripting extracts data from BIM models, while Excel VBA macros process it to calculate environmental impacts, life cycle costs, and GBRS scores. The framework also establishes a systematic mapping of GRIHA-2019 and IGBC appraisal/credit points across life cycle phases and sustainability criteria (procedural, environmental, economic, social, and innovation). The framework is validated with an office building in northwestern India, including uncertainty analysis of BSA parameters. Results highlight that the operational phase is the major contributor to environmental impacts and life cycle costs. The building’s performance in the GRIHA-2019 and IGBC rating systems is evaluated against the combined lens of LCA and LCCA, a perspective that has been limited in prior literature. Discrepancies are observed between the life cycle distribution of LCA impacts and environmental credit allocations in both GBRSs. Climate sensitivity analysis across five Indian climate zones reveals significant variation in life cycle impacts and costs, while GBRS scores remain nearly unchanged, highlighting limited climate responsiveness. The framework culminates in a comprehensive BSA in the Indian context, providing a decision-support system for evaluating sustainable building design strategies, and also identifies certain limitations in the GRIHA-2019 and IGBC rating systems.
{"title":"A BIM-based integrated framework for building sustainability assessment in India: Framework development, implementation, and climate sensitivity analysis","authors":"M.J. Mohammad Nasir , Mayank Suman , P. Ravi Prakash","doi":"10.1016/j.eiar.2025.108318","DOIUrl":"10.1016/j.eiar.2025.108318","url":null,"abstract":"<div><div>The building sector consumes vast resources and energy, contributing significantly to environmental degradation. Addressing these challenges requires Building Sustainability Assessment (BSA) methods such as Life Cycle Assessment (LCA), Life Cycle Cost Analysis (LCCA), and Green Building Rating Systems (GBRSs). This paper presents a BIM-BSA framework in the Indian context, integrating Building Information Modeling (BIM) with LCA, LCCA, and the GRIHA-2019 and IGBC rating systems. Dynamo scripting extracts data from BIM models, while Excel VBA macros process it to calculate environmental impacts, life cycle costs, and GBRS scores. The framework also establishes a systematic mapping of GRIHA-2019 and IGBC appraisal/credit points across life cycle phases and sustainability criteria (procedural, environmental, economic, social, and innovation). The framework is validated with an office building in northwestern India, including uncertainty analysis of BSA parameters. Results highlight that the operational phase is the major contributor to environmental impacts and life cycle costs. The building’s performance in the GRIHA-2019 and IGBC rating systems is evaluated against the combined lens of LCA and LCCA, a perspective that has been limited in prior literature. Discrepancies are observed between the life cycle distribution of LCA impacts and environmental credit allocations in both GBRSs. Climate sensitivity analysis across five Indian climate zones reveals significant variation in life cycle impacts and costs, while GBRS scores remain nearly unchanged, highlighting limited climate responsiveness. The framework culminates in a comprehensive BSA in the Indian context, providing a decision-support system for evaluating sustainable building design strategies, and also identifies certain limitations in the GRIHA-2019 and IGBC rating systems.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108318"},"PeriodicalIF":11.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-27DOI: 10.1016/j.eiar.2025.108323
Haoran Liu , Jingwen Luo , Jingyi Xing , Lena Ciric , Manpreet Bhatti
Plastic pollution is an unavoidable challenge in the process of urban development. It poses significant threats to both residents and urban ecosystems, with reports indicating the presence of plastic in various environmental media. While local governments have made efforts to control plastic pollution, these measures often focus narrowly on disposable plastics and lack comprehensive coordination and guidance. This review aims to address these problems by integrating urban plastic pollution research, management strategies, and policy. The analysis reveals that current urban plastic pollution studies are mainly concentrated in East Asia and Europe. Furthermore, differences are observed in the size, color, and types of plastics across various locations. As an effective comparative analysis tool, meta-analysis demonstrates that plastics of different particle sizes exhibit similar distribution patterns. However, certain urban and suburban areas show plastic concentrations significantly higher than the global average in specific media. An examination of the relationships between GDP, population, and plastic pollution levels indicates no clear global correlation among these factors. However, at continental or national scales, a linear correlation becomes apparent. The review also lists tools and policies that offer practical solutions for managing urban plastic pollution in both developed and developing countries. Additionally, it highlights social forces that can be mobilized to support plastic pollution management. The findings provide targeted recommendations for global urban plastic pollution control, contributing to the development of a comprehensive system that integrates scientific research, investigation, and policy management.
{"title":"Bridging science and policy towards integrated management of Urban plastic pollution: A global review of patterns, drivers, and solutions","authors":"Haoran Liu , Jingwen Luo , Jingyi Xing , Lena Ciric , Manpreet Bhatti","doi":"10.1016/j.eiar.2025.108323","DOIUrl":"10.1016/j.eiar.2025.108323","url":null,"abstract":"<div><div>Plastic pollution is an unavoidable challenge in the process of urban development. It poses significant threats to both residents and urban ecosystems, with reports indicating the presence of plastic in various environmental media. While local governments have made efforts to control plastic pollution, these measures often focus narrowly on disposable plastics and lack comprehensive coordination and guidance. This review aims to address these problems by integrating urban plastic pollution research, management strategies, and policy. The analysis reveals that current urban plastic pollution studies are mainly concentrated in East Asia and Europe. Furthermore, differences are observed in the size, color, and types of plastics across various locations. As an effective comparative analysis tool, meta-analysis demonstrates that plastics of different particle sizes exhibit similar distribution patterns. However, certain urban and suburban areas show plastic concentrations significantly higher than the global average in specific media. An examination of the relationships between GDP, population, and plastic pollution levels indicates no clear global correlation among these factors. However, at continental or national scales, a linear correlation becomes apparent. The review also lists tools and policies that offer practical solutions for managing urban plastic pollution in both developed and developing countries. Additionally, it highlights social forces that can be mobilized to support plastic pollution management. The findings provide targeted recommendations for global urban plastic pollution control, contributing to the development of a comprehensive system that integrates scientific research, investigation, and policy management.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108323"},"PeriodicalIF":11.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Green innovation serves as a vital component for achieving Coordinated Urban-Rural Development (CUD) and the Sustainable Development Goals. This study constructs a multidimensional theoretical framework encompassing technological, institutional, economic, and ecological dimensions. By employing the XGBoost-SHAP model, we analyze the nonlinear associations between multi-dimensional green innovation elements and CUD across 41 cities in China's Yangtze River Delta (2011−2021). The results indicate that green patent granted (GPA) and the proportion of the Internet population (PIP) are pivotal features, exhibiting inflection points at 0.29 and 0.3, respectively. Once these thresholds are exceeded, their contribution to alleviating urban-rural gap increases significantly. Interaction analysis reveals that the concurrent growth of Foreign Direct Investment (FDI) and the Tertiary Industry Proportion (TIP) is linked to an intensified urban-rural gap, highlighting the exclusionary nature of high-end service sector investments. By balancing single-factor threshold regulation with multi-factor synergies, this research identifies optimal combined contribution intervals to establish precise regulatory pathways for green innovation, thereby narrowing the urban-rural divide.
{"title":"Explainable machine learning reveals the nonlinear relationships between green innovation and coordinated urban-rural development: Evidence from the Yangtze River Delta urban agglomeration, China","authors":"Zhonghu Zhang , Rui Wang , Siqi Zhang , Wenqin Meng","doi":"10.1016/j.eiar.2025.108320","DOIUrl":"10.1016/j.eiar.2025.108320","url":null,"abstract":"<div><div>Green innovation serves as a vital component for achieving Coordinated Urban-Rural Development (CUD) and the Sustainable Development Goals. This study constructs a multidimensional theoretical framework encompassing technological, institutional, economic, and ecological dimensions. By employing the XGBoost-SHAP model, we analyze the nonlinear associations between multi-dimensional green innovation elements and CUD across 41 cities in China's Yangtze River Delta (2011−2021). The results indicate that green patent granted (GPA) and the proportion of the Internet population (PIP) are pivotal features, exhibiting inflection points at 0.29 and 0.3, respectively. Once these thresholds are exceeded, their contribution to alleviating urban-rural gap increases significantly. Interaction analysis reveals that the concurrent growth of Foreign Direct Investment (FDI) and the Tertiary Industry Proportion (TIP) is linked to an intensified urban-rural gap, highlighting the exclusionary nature of high-end service sector investments. By balancing single-factor threshold regulation with multi-factor synergies, this research identifies optimal combined contribution intervals to establish precise regulatory pathways for green innovation, thereby narrowing the urban-rural divide.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108320"},"PeriodicalIF":11.2,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-25DOI: 10.1016/j.eiar.2025.108315
Nicola Minafra, Carlo Ingrao, Tiziana Crovella, Annarita Paiano, Giovanni Lagioia
<div><div>Legumes combine a high protein intake with reduced environmental impact and are suitable for application in rotational cropping systems, with the twofold function of producing grains and fixing N into the soil. By doing so, whether put in combination with low-input systems, they can contribute to implementing sustainable agriculture paths. Chickpea is the third most consumed grain legume in the world, and its nitrogen-fixing capacity can be beneficial for the next crops for improving soil fertility, structure, and water retention capacity and for reducing chemical fertilizer production and application. Despite the benefits, it is however needed to explore the relevant environmental sustainability issues associated with chickpea cultivation. To that end, Life Cycle Assessment (LCA) is proven valid methodology to compare cropping system alternatives, to support decision making. In this study, LCA was used in fact to compare conventional vs. organic cultivation of chickpea grains in Southern Italy in the period 2020–2022, through LCA application with a cradle-to-gate approach, using the EF 3.1 method.</div><div>For the assessment, following previously published LCAs,1 kg asported N was chosen as the functional unit (FU), to make allocation possible between the harvested chickpea grains (modelled as kg eq of asported N), and the N leftover, thereby best representing the twofold function of the investigated system to produce legumes and fix N into the soil.</div><div>From a review of the literature, the authors found that only a few LCAs have been developed that dealt with chickpea cultivation, which highlights the relevant contribution that this article is expected to make to specialized literature. This study represents one of the few LCAs focused exclusively on chickpea cultivation, providing a comparative analysis of conventional and organic systems, using an innovative N-based functional unit and an allocation between grain yield and nitrogen fixation.</div><div>With such a FU, organic cultivation resulted to be more environmentally damaging (7.81 mPt vs. 3.40 mPt) than the conventional one, due to its lower yields that amplify the environmental impacts per unit of product. Moreover, a sensitivity analysis was incorporated in the study to explore the extent to which the choice of other FUs influence results from the assessment. The study highlighted, in particular, that results change in favour of the organic system in the case of a surface-based FU, thanks to the reduced agricultural activities and inputs per unit of ha (252 mPt for organic farming, and 279 mPt for conventional farming).</div><div>These findings suggest that, while organic cultivation performs better from an environmental sustainability perspective, conventional farming is more efficient in terms of productivity.</div><div>The study contributed to understanding the importance of FU selection in LCAs and provided valuable insights that can be useful to farmers for impro
豆科植物蛋白质摄入量高，对环境影响小，适合在轮作制度中应用，具有生产谷物和将氮固定到土壤中的双重功能。通过这样做，无论是与低投入系统相结合，它们都可以为实施可持续农业道路作出贡献。鹰嘴豆是世界上消费量第三大的豆科作物，其固氮能力可以为未来作物改善土壤肥力、结构和保水能力以及减少化肥的生产和施用提供有利条件。尽管有好处，但有必要探讨与鹰嘴豆种植相关的环境可持续性问题。为此，生命周期评估（LCA）被证明是比较不同种植制度、支持决策的有效方法。在本研究中，LCA实际上用于比较2020-2022年期间意大利南部鹰嘴豆谷物的传统和有机种植，通过LCA应用于从摇篮到大门的方法，使用EF 3.1方法。在评估中，根据先前发表的LCAs，选择1 kg输运氮作为功能单位（FU），以便在收获的鹰嘴豆籽粒（以输运氮的kg当量为模型）和剩余氮之间进行分配，从而最好地代表所研究系统生产豆类和将氮固定到土壤中的双重功能。通过对相关文献的梳理，作者发现目前仅有少数涉及鹰嘴豆种植的lca，这凸显了本文对专业文献的相关贡献。本研究是为数不多的专注于鹰嘴豆种植的lca之一，采用创新的n基功能单元和籽粒产量与固氮之间的分配，对传统系统和有机系统进行了比较分析。在这样的FU下，有机种植比传统种植对环境的破坏更大（7.81 mPt vs. 3.40 mPt），因为其较低的产量放大了单位产品对环境的影响。此外，研究中还纳入了敏感性分析，以探讨其他FUs的选择对评估结果的影响程度。该研究特别强调，由于减少了每公顷的农业活动和投入（有机农业为2.52亿吨/公顷，传统农业为2.79亿吨/公顷），在基于表面的FU的情况下，结果的变化有利于有机系统。这些发现表明，虽然从环境可持续性的角度来看，有机耕作表现更好，但就生产力而言，传统耕作效率更高。该研究有助于理解FU选择在LCAs中的重要性，并提供了有价值的见解，可以帮助农民提高鹰嘴豆生产系统的可持续性，也可以帮助从业者提高LCA在该研究内容领域的应用。
{"title":"A comparative life cycle assessment between conventional and organic chickpea cultivation in southern Italy","authors":"Nicola Minafra, Carlo Ingrao, Tiziana Crovella, Annarita Paiano, Giovanni Lagioia","doi":"10.1016/j.eiar.2025.108315","DOIUrl":"10.1016/j.eiar.2025.108315","url":null,"abstract":"<div><div>Legumes combine a high protein intake with reduced environmental impact and are suitable for application in rotational cropping systems, with the twofold function of producing grains and fixing N into the soil. By doing so, whether put in combination with low-input systems, they can contribute to implementing sustainable agriculture paths. Chickpea is the third most consumed grain legume in the world, and its nitrogen-fixing capacity can be beneficial for the next crops for improving soil fertility, structure, and water retention capacity and for reducing chemical fertilizer production and application. Despite the benefits, it is however needed to explore the relevant environmental sustainability issues associated with chickpea cultivation. To that end, Life Cycle Assessment (LCA) is proven valid methodology to compare cropping system alternatives, to support decision making. In this study, LCA was used in fact to compare conventional vs. organic cultivation of chickpea grains in Southern Italy in the period 2020–2022, through LCA application with a cradle-to-gate approach, using the EF 3.1 method.</div><div>For the assessment, following previously published LCAs,1 kg asported N was chosen as the functional unit (FU), to make allocation possible between the harvested chickpea grains (modelled as kg eq of asported N), and the N leftover, thereby best representing the twofold function of the investigated system to produce legumes and fix N into the soil.</div><div>From a review of the literature, the authors found that only a few LCAs have been developed that dealt with chickpea cultivation, which highlights the relevant contribution that this article is expected to make to specialized literature. This study represents one of the few LCAs focused exclusively on chickpea cultivation, providing a comparative analysis of conventional and organic systems, using an innovative N-based functional unit and an allocation between grain yield and nitrogen fixation.</div><div>With such a FU, organic cultivation resulted to be more environmentally damaging (7.81 mPt vs. 3.40 mPt) than the conventional one, due to its lower yields that amplify the environmental impacts per unit of product. Moreover, a sensitivity analysis was incorporated in the study to explore the extent to which the choice of other FUs influence results from the assessment. The study highlighted, in particular, that results change in favour of the organic system in the case of a surface-based FU, thanks to the reduced agricultural activities and inputs per unit of ha (252 mPt for organic farming, and 279 mPt for conventional farming).</div><div>These findings suggest that, while organic cultivation performs better from an environmental sustainability perspective, conventional farming is more efficient in terms of productivity.</div><div>The study contributed to understanding the importance of FU selection in LCAs and provided valuable insights that can be useful to farmers for impro","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108315"},"PeriodicalIF":11.2,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1016/j.eiar.2025.108311
Shuang Liu, Yawen Wang, Chao Ren
Climate change has resulted in warm winters with more frequent and severe temperature fluctuations, which pose remarkable challenges in assessing the new patterns of cold weather in varying climates. Such emerging winter patterns, characterized by factors like sudden temperature transitions from warm to cold, are less accounted for in current cold event assessments, leaving their health impacts unclear. In this study, we conducted a systematic review to: (1) categorize and compare the strengths and weaknesses of each cold event definition across climates, and evaluate how these methodological differences affect comparability of health outcomes; (2) qualitatively assess the suitability of existing cold event definitions in describing different extreme cold events; and (3) classify cold-health implications under various cold event definition types in order to guide the enhancement of cold weather warning systems across climates. A total of 163 studies from 48 countries across 15 Köppen climate zones were identified by March 2025, which analyzed the effects of cold on 58 different physiological and psychological health outcomes. The variations in adverse health impacts were attributed to different cold event definitions, reference periods, confounders, contextual climate, and population susceptibility to cold events. Current cold event definitions are inadequate for describing more frequent winter temperature fluctuation events, a pattern observed in over 60 % of the global area. The disparity between the cold event definitions used in research and those used by Cold Weather Warning systems (CWWs) restricts the applicability of current insights for CWWs refinement. Based on these understandings of urban-scale climate change and emerging winter patterns, a framework was proposed for future studies and cold-related service providers to develop suitable methods for assessing cold events-related health impacts that support early interventions.
{"title":"Cold weather patterns and health impacts across climate regions in a warming world: A systematic review at the global scale","authors":"Shuang Liu, Yawen Wang, Chao Ren","doi":"10.1016/j.eiar.2025.108311","DOIUrl":"10.1016/j.eiar.2025.108311","url":null,"abstract":"<div><div>Climate change has resulted in warm winters with more frequent and severe temperature fluctuations, which pose remarkable challenges in assessing the new patterns of cold weather in varying climates. Such emerging winter patterns, characterized by factors like sudden temperature transitions from warm to cold, are less accounted for in current cold event assessments, leaving their health impacts unclear. In this study, we conducted a systematic review to: (1) categorize and compare the strengths and weaknesses of each cold event definition across climates, and evaluate how these methodological differences affect comparability of health outcomes; (2) qualitatively assess the suitability of existing cold event definitions in describing different extreme cold events; and (3) classify cold-health implications under various cold event definition types in order to guide the enhancement of cold weather warning systems across climates. A total of 163 studies from 48 countries across 15 Köppen climate zones were identified by March 2025, which analyzed the effects of cold on 58 different physiological and psychological health outcomes. The variations in adverse health impacts were attributed to different cold event definitions, reference periods, confounders, contextual climate, and population susceptibility to cold events. Current cold event definitions are inadequate for describing more frequent winter temperature fluctuation events, a pattern observed in over 60 % of the global area. The disparity between the cold event definitions used in research and those used by Cold Weather Warning systems (CWWs) restricts the applicability of current insights for CWWs refinement. Based on these understandings of urban-scale climate change and emerging winter patterns, a framework was proposed for future studies and cold-related service providers to develop suitable methods for assessing cold events-related health impacts that support early interventions.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108311"},"PeriodicalIF":11.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1016/j.eiar.2025.108316
Seied Mehdy Hashemy Shahdany , Reza Rejaie
Under non-cooperative groundwater (GW) abstraction, irrigation districts require decision frameworks that couple predictive skill with implementable, auditable operating rules. A machine-learning framework was developed to replace manual standard operating procedures (SOPs) with cluster-informed surrogate SOPs derived from predictive models and regime (cluster) analysis. The System of Environmental–Economic Accounting for Water (SEEA-Water) physical supply–use information was integrated with Random Forest (RF) and Multi-Layer Perceptron (MLP) predictors, and human-readable surrogate policies were extracted subject to operational constraints. Three SOP designs of increasing physical realism (SOP-A, SOP-B, and SOP-C) were evaluated in the Nekoo-Abad Irrigation District, Iran, using a blocked chronological split (training: 2000–2016; validation: 2017–2020; testing: 2021–2024), which was common to all models. Incorporation of seepage and operational losses, as well as reservoir-release limits (SOP-C), improved predictive skill relative to simpler designs. On the held-out test set, nonlinear models outperformed the linear baseline, with RF/MLP achieving R2 values of around 0.94–0.95 and RMSE values of around 0.08 m3/s, compared to R2 values of around 0.91 for multiple linear regression; robustness was maintained under drought-depleted regimes. Regime clustering further stabilized generalization by grouping days into homogeneous operational states. Feature attribution identified GW extraction and operational losses as the dominant drivers of return-flow variability, indicating actionable levers for management. Surrogate SOPs closely reproduced the machine-learning recommendations while remaining interpretable and constraint-aware, thereby translating data-driven predictions into deployable rules. The framework advances transparent, quantitatively validated decision support for conjunctive surface- and GW operations, enhancing reliability and adaptability under hydrologic variability and non-cooperative pumping.
{"title":"Integrating SEEA-water accounting and machine learning for transparent SOP reform under non-cooperative groundwater use in irrigation districts","authors":"Seied Mehdy Hashemy Shahdany , Reza Rejaie","doi":"10.1016/j.eiar.2025.108316","DOIUrl":"10.1016/j.eiar.2025.108316","url":null,"abstract":"<div><div>Under non-cooperative groundwater (GW) abstraction, irrigation districts require decision frameworks that couple predictive skill with implementable, auditable operating rules. A machine-learning framework was developed to replace manual standard operating procedures (SOPs) with cluster-informed surrogate SOPs derived from predictive models and regime (cluster) analysis. The System of Environmental–Economic Accounting for Water (SEEA-Water) physical supply–use information was integrated with Random Forest (RF) and Multi-Layer Perceptron (MLP) predictors, and human-readable surrogate policies were extracted subject to operational constraints. Three SOP designs of increasing physical realism (SOP-A, SOP-B, and SOP-C) were evaluated in the Nekoo-Abad Irrigation District, Iran, using a blocked chronological split (training: 2000–2016; validation: 2017–2020; testing: 2021–2024), which was common to all models. Incorporation of seepage and operational losses, as well as reservoir-release limits (SOP-C), improved predictive skill relative to simpler designs. On the held-out test set, nonlinear models outperformed the linear baseline, with RF/MLP achieving R<sup>2</sup> values of around 0.94–0.95 and RMSE values of around 0.08 m<sup>3</sup>/s, compared to R<sup>2</sup> values of around 0.91 for multiple linear regression; robustness was maintained under drought-depleted regimes. Regime clustering further stabilized generalization by grouping days into homogeneous operational states. Feature attribution identified GW extraction and operational losses as the dominant drivers of return-flow variability, indicating actionable levers for management. Surrogate SOPs closely reproduced the machine-learning recommendations while remaining interpretable and constraint-aware, thereby translating data-driven predictions into deployable rules. The framework advances transparent, quantitatively validated decision support for conjunctive surface- and GW operations, enhancing reliability and adaptability under hydrologic variability and non-cooperative pumping.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108316"},"PeriodicalIF":11.2,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.eiar.2025.108314
Shijia Chong , Jing Wu , I-Shin Chang
To cope with the surging global energy demands and escalating environmental challenges, the sustainable development of the photovoltaic (PV) industry has become a pivotal solution for balancing energy security and ecological conservation. This study fills a critical gap in assessing China's PV industry sustainability by developing a context-adaptive multi-driver model, which systematically integrates economic, environmental, and resource dimensions and optimizes for China's regional heterogeneity and industrial constraints. The model incorporates core drivers (energy transition imperatives, technological advancements, economic incentives) and limiting factors (policy uncertainties, market competition pressures, land resource constraints) in the development of the PV industry to elucidate the multi-dimensional benefits, applying it to a provincial empirical analysis of sustainable potential. Findings reveal China's PV energy exhibits significant regional disparities, with economic costs ranging from 0.56 to 1.27CNY/kWh. A 1 kW PV plant generates annual environmental benefits of 37.24 to 655.19CNY, with developed regions demonstrating distinct carbon mitigation advantages. Land use benefits generally decline from southeast to northwest China. Employing the multi-driver evaluation model, the sustainability index (SI) of China's PV industry ranges from 0.09 to 0.83, with higher values in southeastern coastal regions compared to western inland areas. Thirty provinces are classified into three zones: Advantage Spearheading Zone, Potential Unleashing Zone, and Resource Unactivated Zone. Zone-specific strategies are proposed to promote the high-quality and sustainable development of the PV industry.
{"title":"Sustainable development of photovoltaic industry in China: An innovative multi-driver model","authors":"Shijia Chong , Jing Wu , I-Shin Chang","doi":"10.1016/j.eiar.2025.108314","DOIUrl":"10.1016/j.eiar.2025.108314","url":null,"abstract":"<div><div>To cope with the surging global energy demands and escalating environmental challenges, the sustainable development of the photovoltaic (PV) industry has become a pivotal solution for balancing energy security and ecological conservation. This study fills a critical gap in assessing China's PV industry sustainability by developing a context-adaptive multi-driver model, which systematically integrates economic, environmental, and resource dimensions and optimizes for China's regional heterogeneity and industrial constraints. The model incorporates core drivers (energy transition imperatives, technological advancements, economic incentives) and limiting factors (policy uncertainties, market competition pressures, land resource constraints) in the development of the PV industry to elucidate the multi-dimensional benefits, applying it to a provincial empirical analysis of sustainable potential. Findings reveal China's PV energy exhibits significant regional disparities, with economic costs ranging from 0.56 to 1.27CNY/kWh. A 1 kW PV plant generates annual environmental benefits of 37.24 to 655.19CNY, with developed regions demonstrating distinct carbon mitigation advantages. Land use benefits generally decline from southeast to northwest China. Employing the multi-driver evaluation model, the sustainability index (SI) of China's PV industry ranges from 0.09 to 0.83, with higher values in southeastern coastal regions compared to western inland areas. Thirty provinces are classified into three zones: Advantage Spearheading Zone, Potential Unleashing Zone, and Resource Unactivated Zone. Zone-specific strategies are proposed to promote the high-quality and sustainable development of the PV industry.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108314"},"PeriodicalIF":11.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.eiar.2025.108313
Yuwei Zhao , Lu Chen , Wei Liu , Huiying Yang , Shuai Li , Xiaowei Cui , Zhaojie Cui
Straw, an agricultural byproduct, presents substantial environmental challenges due to its vast production volume and low utilization efficiency. Identifying low-carbon, scalable utilization pathways remains a globally significant challenge. China, the world’s largest producer and emitter of crop straw, continues to rely on inefficient disposal methods such as burning and uncontrolled stacking, highlighting the need for strategic management solutions. In this study, we focus on three representative types of straw—corn, wheat, and rice—employing an integrated life cycle assessment and cost–benefit analysis to quantitatively evaluate both the carbon mitigation potential and economic performance of nine straw-based resource utilization technologies. Results indicate that biogasification (BI) has the highest carbon mitigation potential, whereas hydrothermal carbonization yields superior economic returns. Scenario analysis reveals that regional resource endowments, policy incentives, and technological adoption rates significantly influence the optimal choice of straw utilization pathways. BI technology emerges as the most promising approach for delivering environmental and economic co-benefits at scale. Nationwide adoption of BI technology could reduce annual emissions by approximately 800 million tCO₂e, equivalent to a 73.2% mitigation increase over the baseline scenario, while increasing net profits by 76.1%. These findings provide actionable insights for China’s straw management strategies and offer implementable pathways to align circular agriculture with carbon peaking and neutrality goals.
{"title":"Multi-source straw used as a resource can reduce environmental and economic burdens","authors":"Yuwei Zhao , Lu Chen , Wei Liu , Huiying Yang , Shuai Li , Xiaowei Cui , Zhaojie Cui","doi":"10.1016/j.eiar.2025.108313","DOIUrl":"10.1016/j.eiar.2025.108313","url":null,"abstract":"<div><div>Straw, an agricultural byproduct, presents substantial environmental challenges due to its vast production volume and low utilization efficiency. Identifying low-carbon, scalable utilization pathways remains a globally significant challenge. China, the world’s largest producer and emitter of crop straw, continues to rely on inefficient disposal methods such as burning and uncontrolled stacking, highlighting the need for strategic management solutions. In this study, we focus on three representative types of straw—corn, wheat, and rice—employing an integrated life cycle assessment and cost–benefit analysis to quantitatively evaluate both the carbon mitigation potential and economic performance of nine straw-based resource utilization technologies. Results indicate that biogasification (BI) has the highest carbon mitigation potential, whereas hydrothermal carbonization yields superior economic returns. Scenario analysis reveals that regional resource endowments, policy incentives, and technological adoption rates significantly influence the optimal choice of straw utilization pathways. BI technology emerges as the most promising approach for delivering environmental and economic co-benefits at scale. Nationwide adoption of BI technology could reduce annual emissions by approximately 800 million tCO₂e, equivalent to a 73.2% mitigation increase over the baseline scenario, while increasing net profits by 76.1%. These findings provide actionable insights for China’s straw management strategies and offer implementable pathways to align circular agriculture with carbon peaking and neutrality goals.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"118 ","pages":"Article 108313"},"PeriodicalIF":11.2,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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