Search Results (58)

The mining industry is a cornerstone of global energy security and industrial supply chains, yet its resilience to systemic disruptions such as COVID-19 has been critically overlooked. This disruption provided a rare opportunity to trace supply-chain impacts. Using an environmentally extended multi-regional input-output (EEMRIO) model integrated with a Criteria Importance Through Intercriteria Correlation (CRITIC) weighted approach. We compare pandemic trajectories with counterfactual no-pandemic trajectories: the no-pandemic model (calibrated on 2004–2018) projects 2019–2025, while the pandemic model (calibrated through 2023) projects 2023–2025, with 2023 serving as the observed baseline and transition year. Our findings reveal a transient reduction in mining water and carbon footprints, juxtaposed with stark economic contractions: mining value fell by 40% in China, 37% in India, and 13% in the United States. The weighted component among the water-carbon-value (WVC) analysis further uncovers a tripolar spatial pattern, categorizing countries into financial hubs with high value, such as Switzerland, carbon-locked exporters like Brunei, and water-stressed regions, including Cambodia. Despite absorbing substantial embodied environmental burdens, China maintained its position as the global value hub. These insights underscore the urgency of policies that enhance structural efficiency, decarbonize the power sector, and foster supply chain diversification to decouple economic value from environmental pressures while mitigating spatial inequalities. Full article

Artificial intelligence (AI) is commonly assessed through the electricity used by models and data centres, leaving the carbon transferred through the wider production system insufficiently resolved. We disaggregate China’s AI industry from the broader information and communication technology sector and construct comparable 31-sector environmentally extended input–output accounts for 2015, 2017, 2020 and 2023. Embodied-emission accounting is integrated with linkage analysis, structural path analysis and structural decomposition analysis to quantify the footprint, locate critical supply-chain pathways and explain temporal change. AI-related embodied CO₂ emissions increased from 140.03 Mt in 2015 to 324.37 Mt in 2023. Indirect emissions reached 187.01 Mt in 2023 and remained higher than direct emissions, while backward linkages consistently exceeded forward linkages. Approximately half of the footprint was concentrated within the first three production tiers, with other ICT, electricity and heat, transport, metals and non-metallic minerals forming the largest short paths. In the current-price SDA, domestic final demand was associated with a 138.82 Mt increase between 2020 and 2023, outweighing the 20.24 Mt reduction from production-structure change. The results recast AI decarbonisation as a systems-governance problem: operational efficiency must be coupled with demand management, low-carbon electricity, hardware circularity and targeted upstream procurement. Full article

Tourism is carbon-intensive, with transport and mobility driving much of its greenhouse-gas emissions, yet destination-level evidence on carbon hotspots and decarbonization from a supply-chain perspective remains scarce, especially in developing regions. This study examines low-carbon tourism supply chain management in seven purposively selected provinces of Northeastern Thailand (Isan) using a qualitative-dominant convergent mixed-methods design that integrates demand- and supply-side evidence. A visitor survey yielded 74 open-ended responses (60 complete questionnaires), alongside seven semi-structured key-informant interviews with tourism supply chain operators across the seven provinces. Quantitative data were analyzed with descriptive statistics, and qualitative data with thematic analysis using qualitative data analysis software, drawing on code-frequency and co-occurrence analysis; the two strands were triangulated in joint displays. Visitors reported moderate satisfaction (grand mean 3.91 on a five-point scale) but rated environmental management and safety lowest, engaging with sustainability through visible service cues rather than emissions. Key informants identified perceived carbon hotspots, carrying capacity, and seasonality as dominant concerns, attributing the destination’s footprint chiefly to transport dependence (informant-reported estimates ranging from approximately 80% to nearly 100% private-car arrivals, amid limited public transport) and accommodation energy; these hotspots reflect stakeholder perceptions rather than measured emissions, as no carbon accounting, environmentally extended input–output analysis, or life-cycle assessment was conducted. Integration revealed a demand–supply perception gap and five proposed, interdependent low-carbon pathways: coordinated mobility, accommodation energy efficiency, strengthened local procurement, carrying-capacity and waste management, and multi-stakeholder governance. The findings offer a developing-region, supply-chain-oriented basis for future tourism decarbonization research and practice. Full article

Data Envelopment Analysis (DEA) is widely applied to evaluate technological efficiency in the energy sector, yet its results are often difficult to interpret, particularly when extended model specifications are used. This study investigates how alternative treatments of environmental conditions and undesirable outputs influence efficiency measurement in energy technologies. Four DEA specifications are compared: the classical CCR model, a model incorporating non-discretionary inputs (CCR-ND), a model including undesirable outputs (CCR-B), and a combined specification (CCR-ND-B). The empirical analysis is based on data describing coal gasification technologies and is supplemented with controlled hypothetical cases designed to isolate the effects of environmental parameters. The results show that incorporating non-discretionary inputs and undesirable outputs does not necessarily reduce efficiency scores but reshapes the geometry of the production possibility set and modifies the structure of benchmark technologies. The findings highlight the importance of careful classification of inputs and outputs and emphasize that DEA results should be interpreted in relation to the underlying modeling assumptions. Comparing alternative model specifications improves transparency and helps avoid treating DEA as a “black box”, supporting more informed efficiency assessment in energy technology analysis. Full article

Mitigating trade-embodied carbon is essential for the sustainable, low-carbon transition of China’s manufacturing sector amid increasingly integrated domestic and global production networks. This study measures total trade-embodied carbon, embodied carbon outflows, and embodied carbon exports within a China-embedded global multi-regional input–output framework. Using a panel dataset covering 30 provinces, 15 manufacturing industries, and 7 benchmark years from 2002 to 2020, the study employs high-dimensional fixed-effects models to examine the effect of green finance—defined as finance directed toward environmentally sustainable and low-carbon activities—on trade-embodied carbon. The results show that green finance significantly reduces trade-embodied carbon, with a relatively stronger effect in the domestic trade dimension. Mechanistic analysis indicates that this effect operates through both technological and structural channels. Heterogeneity analysis further suggests that the carbon mitigation effect of green finance is more pronounced in the eastern and central regions and in energy-intensive industries. This study extends the analysis of the environmental effects of green finance from the value-chain trade perspective and provides empirical evidence to advance the low-carbon transition of manufacturing under intertwined domestic and global production networks. Full article

This review paper synthesizes the application and evolution of environmentally extended input–output (EEIO) analysis in agricultural research, drawing on 647 publications (Scopus and Web of Science, 1978–2025) following the PRISMA method and using the Bibliometrix package in the R statistical computing environment. EEIO has become a leading method for assessing system-level environmental impacts by quantifying direct and indirect flows across complete supply chains. Bibliometric and thematic analyses reveal accelerated growth since 2015 and four principal domains of enquiry: emissions embodied in trade, water-resource management, energy and climate impacts, and the sustainability of agri-food supply chains. EEIO’s principal value lies in its capacity to support production- versus consumption-based accounting and to reveal intersectoral trade-offs that single-sector approaches overlook. However, standard EEIO frameworks remain constrained by fixed technical coefficients, coarse sectoral aggregation, and uncertainty in environmental extensions, which limit their capacity to resolve farm-scale processes, structural change, and feedbacks. To enhance analytical rigor and policy relevance, we advocate hybridization with life-cycle and farm-level data, development of higher-resolution multi-regional EEIO tables, incorporation of stochastic and scenario analyses, dynamic formulations to capture technological change, and adoption of open-data standards with transparent reporting. Advancing these priorities will improve comparability, reproducibility and the practical uptake of EEIO for evidence-based transitions in agricultural systems. Full article

The sustainability of the wine sector depends on primary production practices and on the adaptability of plant material to climate change. This study evaluates the carbon footprint and technical efficiency of four grape varieties in Paionia using an integrated Life Cycle Assessment and Data Envelopment Analysis framework. A cradle-to-gate approach was adopted, with system boundaries extending from input production to harvest, and functional units of kg CO₂e/ha to capture input intensity and kg CO₂e/kg grape to assess product-level environmental efficiency. The analysis included 82 vineyards, with DEA scores ranging from 0.744 to 1.000; most vineyards operated below the efficiency frontier, and the input-oriented VRS model identified potential input reductions without affecting output. Merlot showed the highest footprint (3794.02 kg CO₂e/ha), followed by Assyrtiko (2798.40) and Xinomavro (2784.48), while Roditis had the lowest (1958.07); on a per-kg basis, emissions were 0.340, 0.304, 0.281, and 0.143 kg CO₂e/kg respectively. The DEA identified targeted input-saving opportunities, including reduced irrigation needs in white varieties and lower nutrient and plant-protection requirements in red varieties, while the strong performance of Roditis highlights the advantages of locally adapted, low-input plant material for improving efficiency. Full article

Sustained innovation is pivotal for establishing long-term technological advantages and ensuring corporate sustainability, which holds particular significance for “specialized, refined, unique, and innovative” (SRUI) enterprises that concentrate on niche segments and are innovation-intensive. Grounded in signaling theory and principal–agent theory, and situated within the practical context of financing constraints, this paper investigates how environmental, social, and governance (ESG) performance contributes to sustaining innovation in such firms. Using panel data from Chinese SRUI enterprises between 2010 and 2023, we measure sustained innovation along two dimensions: sustained innovation input and sustained innovation output. The results demonstrate that ESG performance significantly enhances sustained innovation among SRUI enterprises. Mechanism analysis reveals that ESG operates through three pathways: optimizing talent structure, mitigating managerial myopia, and strengthening working capital management. Heterogeneity tests further indicate that the positive effect of ESG on overall innovation sustainability is stronger with a younger management team and lower government subsidies. Moreover, in firms with heightened climate risk perception, ESG strongly promotes the sustained innovation input but exhibits a weaker effect on the continuity of innovative output. In enterprises with stronger big-data technology application capabilities, ESG significantly improves the continuity of patent output yet does not significantly affect the continuity of innovative input. This study extends the literature on the economic consequences of ESG from the perspective of sustained innovation, while providing new mechanistic evidence for understanding how highly specialized small and medium-sized enterprises build long-term innovation capacity. Full article

Water scarcity is increasingly shaped by interactions between environmental constraints and interconnected economic systems, evolving from a localized supply–demand issue into a systemic risk embedded in economic networks. This study develops an integrated framework that conceptualizes water scarcity as a multidimensional risk by jointly accounting for water quantity, water quality, and environmental flow requirements, and embeds it within a multiregional input–output (MRIO) model to examine its formation and transmission across China. Results show that multidimensional constraints substantially amplify water scarcity risk and reshape its spatial distribution, extending risk beyond traditionally water-stressed regions to major agricultural provinces and key ecological function zones. Water-intensive, pollution-intensive, and basic industries form the core of risk accumulation, while virtual water linkages drive cross-regional risk propagation, with developed coastal provinces acting as major receivers. Network analysis identifies a small number of provinces—particularly Henan and Jiangsu—as critical hubs for risk transmission and systemic amplification. These findings highlight the need for integrated, multidimensional, and network-oriented water governance to enhance water system resilience. Full article

The decarbonization of the building sector is a critical challenge for achieving Japan’s net-zero targets. However, comprehensive assessments comparing residential construction methods and building heights at the national scale remain limited. This study applies Environmentally Extended Input–Output Analysis (EEIOA) to evaluate the embodied CO₂ emissions associated with four distinct residential construction methods. The results reveal that, when accounting for carbon storage, the net CO₂ emissions per unit of floor area were significantly lower for wooden houses (195 kg-CO₂/m²) compared to steel-reinforced concrete (1109 kg-CO₂/m²), reinforced concrete (857 kg-CO₂/m²), and steel-framed houses (803 kg-CO₂/m²). A further analysis based on building height indicates a structural divergence: while wooden houses account for the majority of emissions in one- to three-story buildings due to their high market share, reinforced concrete houses dominate emissions in four- to nine-story buildings driven by their high carbon intensity. These findings suggest that promoting timber construction, particularly in taller buildings, is a vital strategy for climate change mitigation. Consequently, policy support focusing on technological advancement, cost reduction, and consumer awareness is essential to accelerate the adoption of wooden architecture. Full article

Within the context of China’s green economy aimed at sustainable development, research on the linkage between water resources and industry has garnered considerable attention in the academic community. However, the impact of total body water (TBW) transfer and allocation embodied in the labor force—the primary economic actors—has not been addressed in the economic sector. On methodology, the “EEIO-SDA-SPD-II” (ISSI) model employed in this study encompasses measurements methods, such as an environmentally extended input–output model (EEIO), structural decomposition analysis (SDA), structural path decomposition (SPD), and the imbalance index (II), to explore the crucial paths, driving factors, and distribution of water transfer in TWB spanning 15 Chinese industries between 2007 and 2022. The findings indicate that the shifts in TBW in the manufacturing sector are more discernible when viewed through the lens of social driving factors. The construction business exhibits the most significant increase in male total body water (MTBW), whereas the education sector reflects the rapid growth in female total body water (FTBW). Pertaining to final demand, domestic consumption constitutes the primary contributor category to the increase in TWB, followed by fixed capital formation and exports. According to the SPD results, the construction sector exerts the greatest influence on the transfer of MTBW, while the education sector is characterized by the highest path coefficient value for FTBW. In contrast, the manufacturing sector shows the most pronounced initial path. Based on the imbalance index analysis, agriculture derives the greatest economic gains from TBW input, whereas the education sector yields the lowest. Full article

This study aimed to explore the trade-offs between regional economic activity and environmental policy to explore economic approaches for reducing and managing pollutant discharge while maintaining a balance between socioeconomic activities and the marine environment. A linear programming simulation was conducted to model the interactions between socioeconomic activities, pollutant emissions, and reduction policies in the Omura Bay watershed. The model was designed to maximize Gross Regional Product (GRP), using inflow pollutant loads as a constraint. The simulation showed that a 12.7% reduction in 2015 pollutant loads is feasible under total load control. However, this level of reduction would cause a 14% decrease in watershed GRP. Further analysis revealed that reductions beyond 12.4% would significantly lower GRP and increase the cost of mitigation, making a 12.3% reduction the most realistic upper limit. The estimated cost of implementing countermeasures to manage pollutant inflow was JPY 6.7 billion, which would translate to a JPY 37.6 billion reduction in the cost to maintain current conditions and a JPY 26.7 billion reduction in the maximum reduction scenario (12.3%) with minimal economic impact. This analysis highlights the tradeoff between environmental protection and economic performance. A key innovation is the proposal of a “proper nutrient management” scenario, moving beyond uniform reductions to assess region-specific targets that consider ecological needs, such as those of the fishery industry. This approach emphasizes the importance of setting realistic and ecologically balanced reduction targets. Full article

Tourism is a fast-growing sector that generates a significant greenhouse gas (GHG) footprint, yet subnational data needed to measure the sector remain scarce. Quantifying tourism-related emissions is essential for effective climate policy and alignment with international targets. This study contributes to quantifying tourism sector GHG emissions using the 2023 Korean National Travel Survey data and a spend-based environmentally extended input–output (EEIO) model. Expenditure data were mapped onto the 33-sector multiregional EEIO framework, estimating a total of 2623 tCO_2eq emissions by region, expenditure type, and industry sector in 2023, where about 73% of the total was attributed to tourism-related sectors with the sample data, 24,282. The results illustrate how tourism emissions are shaped especially by transportation systems and regional context. Provinces that surround metropolitan cities in the mainland, for example, Gyeonggi and Gangwon Provinces near Seoul and Incheon, and Gyeongnam Province neighboring Busan and Ulsan, record higher emissions due to large travel volumes from these metropolitan cities and energy-intensive transportation services. Jeju Island stands out as an outlier, with disproportionately high emissions relative to its size, driven by reliance on aviation, which significantly raises its per-visitor footprint. Sectoral analysis identified transportation services, agriculture, electricity, and gas as key sectors. By providing detailed provincial-level data, this study offers a first empirical foundation to corporate Category 6 of Scope 3 reporting and supports central and local governments in designing region-specific climate strategies associated with tourism-related sectors. Full article

Reducing food loss and waste (FLW) is a global priority under UN SDG 12.3, yet Taiwan has lacked stage-specific FLW data and systematic valuation of its environmental and economic implications. This study addresses these gaps by integrating localized FLW estimates from the APEC-FLOWS database with an enhanced analytical framework—the Environmentally Extended Input–Output Valuation (EEIO-V) model. The EEIO-V extends conventional input–output analysis by monetizing multiple environmental burdens, including greenhouse gases, air pollutants, wastewater, and solid waste, thereby linking FLW reduction to tangible economic benefits and policy design. The simulations reveal substantial differences in environmental cost reductions across supply chain stages, with downstream interventions delivering the largest benefits, particularly in reducing air pollution and greenhouse gases. By contrast, upstream measures contribute relatively smaller improvements. These findings highlight the novelty of EEIO-V in bridging environmental valuation with system-level FLW analysis, and they provide actionable insights for designing cost-effective, stage-specific strategies that prioritize downstream interventions to advance Taiwan’s sustainability and policy goals. Full article

As global pressure to reduce emissions intensifies, China is increasingly turning to digital technologies to drive sustainable industrial development, aiming to boost production while keeping carbon emissions in check. This study takes a micro-level approach by dividing the industry into 17 sectors and applying an environmentally-extended input–output (EEIO) model combined with structural decomposition analysis (SDA) to quantify the impact of digital transformation on carbon emissions across sectors. This study used input–output data from 2012 and 2017. The results indicate that (1) technological improvements driven by digitalization play a key role in reducing industrial carbon emissions, and (2) while high-carbon sectors show substantial emission reductions due to digital transformation, industries such as textiles—where digital adoption is more challenging—exhibit only limited improvements. These findings underscore the need to further advance technological upgrading and transformation in less digitally integrated sectors. Full article