Search Results (5)

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

Eco-efficiency analysis can provide useful information about sustainability in the tourism industry, which has an important role in both global economy recovery and Sustainable Development Goals (SDGs), generating considerable indirect carbon emissions with respect to the supply chain due to its significant connections to other industries. This study, from the perspective of tourism sectors, including tourism hotels, travel agencies, and scenic spots, integrated the environmentally extended input–output analysis (EEIO) and data envelopment analysis (DEA) models to develop a research framework, analyzing the indirect carbon emissions of the tourism supply chain, evaluating eco-efficiency with respect to both direct carbon emissions and total carbon emissions (including direct and indirect parts), and exploring the driving factors of eco-efficiency of tourism sectors using Tobit regression models. This study took Gansu as a case, a province in China characterized by higher carbon intensity, an underdeveloped economy, and rapid tourism growth. The results demonstrate that (1) tourism hotels contribute the most carbon emissions in tourism sectors, especially indirectly due to the supply chain, with carbon emissions mainly resulting from the manufacturing of food and tobacco; (2) the eco-efficiency of tourism sectors in Gansu presents a U-shaped curve, which is consistent with Kuznets’ theory; and (3) energy technology is key to improving the eco-efficiency of tourism sectors. The research results provide a clear path for the reduction of carbon emissions and the improvement of eco-efficiency in Gansu tourism sectors. Against the backdrop of global climate change and the post-COVID-19 era, our research framework and findings provide a reference for similar regions and countries who are in urgent need of rapid tourism development to effect economic recovery. Full article

With the Paris Agreement, countries are obliged to report greenhouse gas (GHG) emission reductions, which will ensure that the global temperature increase is maintained well below 2 °C. The parties will report their nationally determined contributions (NDCs) in terms of plans and progress towards these targets during the postponed COP26 (Conference of the Parties under the UNFCCC) in Glasgow in November 2021. These commitments, however, do not take significant portions of the consumption-related emissions related to countries imports into account. Similarly, the majority of companies that report their emissions to CDP (Formerly Carbon Disclosure Project) also do not account for their embodied value-chain-related emissions. Municipalities, on the path towards carbon neutrality in accordance with the methods outlined by C40, also do not include imported and embodied CO₂ in their total emission tallies. So, who is responsible for these emissions—the producer or the consumer? How can we ensure that the NDCs, municipalities’ and companies’ reduction targets share the responsibility of the emissions in the value chain, thus ensuring that targets and plans become sustainable, climate fair, and just in global value chains? Today the responsibility lays with the producer, which is not sustainable. We have the outline for the tools needed to quantify and transparently share the responsibility between producers and consumers at corporate, municipal and national levels based on an improved understanding of the attendant sources, causes, flows and risks of GHG emissions globally. Hybrid life cycle analysis/environmentally extended input–output (LCA/EEIO) models can for example be further developed. This will, in the end, enable everyday consumption to support a more sustainable, green and low carbon transition of our economy. Full article

Environmentally-extended input-output (EEIO) analysis provides a simple and robust method for evaluating the linkages between economic consumption activities and environmental impacts, including the harvest and degradation of natural resources. EEIO is now widely used to evaluate the upstream, consumption-based drivers of downstream environmental impacts and to evaluate the environmental impacts embodied in goods and services that are traded between nations. While the mathematics of input-output analysis are not complex, straightforward explanations of this approach for those without mathematical backgrounds remain difficult to find. This manuscript provides a conceptual and intuitive introduction to the goals of EEIO, the principles and mathematics behind EEIO analysis and the strengths and limitations of the EEIO approach. The wider adoption of EEIO approaches will help researchers and policy makers to better measure, and potentially decrease, the ultimate drivers of environmental degradation. Full article