Input-output Life Cycle Assessment Research Articles

Life Cycle Assessment Framework for Diagnostic Imaging

The increasing focus on environmental sustainability is becoming essential in the diagnostic imaging sector, which is accredited for about 10% of the healthcare industry’s carbon footprint. A multitude of research initiatives investigated the environmental impacts of diagnostic imaging, encompassing factors like electricity consumption, carbon emissions, and waste generation from these procedures. Life Cycle Assessment (LCA) stands as a prominent method for structural assessment of environmental impacts, offering a detailed framework for examining the environmental consequences of specific processes. The aim of this study includes analyzing existing LCA approaches in literature to identify their limitations and to suggest an elaborate framework for LCA application in diagnostic imaging.Out of 17 original articles on environmental sustainability in radiology published since 2014, but only a part, 29.4% (5/17), described an LCA approach. The different characteristics of these studies provide valuable insights, enabling the proposal of a more comprehensive LCA research methodology.The reviewed articles did not present a uniform research outcome. According to the GreenHouse Gas (GHG) Protocol Corporate Standard, the optimal outcome for assessing environmental impact is the calculation of greenhouse gas emissions. For a thorough methodological approach in LCA, it is essential to cover all direct and indirect emissions associated with diagnostic imaging. All studies (100%, 5/5) considered the electricity consumption of imaging equipment. Usage of consumables was included in 80% (4/5) of the studies. Only 40% (2/5) of articles considered auxiliary equipment like computers and contrast-medium injectors, as well as heating, ventilation and air conditioning (HVAC) systems. Factors like equipment manufacture, staff travel, and waste generation, though crucial to overall greenhouse gas emissions, were each covered in only 20% (1/5) of the studies. The articles also varied in their LCA versions, with two employing the detailed Cradle-to-Grave approach, while others used partial Cradle-to-Gate and Input-Output LCA methods.The insights from this analysis could lead to a valuable framework for a new LCA methodological approach in diagnostic imaging. This novel approach is designed to overcome the limitations observed in existing research, offering a more comprehensive analysis. By enhancing the LCA framework, it will be possible identify the phases in diagnostic imaging that have the most substantial environmental impact, allowing for the development of more targeted strategies to reduce GHG emissions associated with diagnostic procedures.

Environmental impact assessment and remediation decision-making of a contaminated megasite: Combining LCA and IO-LCA

Environmental impact assessment is crucial for the green and sustainable development of contaminated sites. Although life cycle assessment (LCA) is a widely used quantitative approach for environmental impact assessment, its application in the context of megasite remediation is relatively limited and does not consider impacts on the entire industry chain. In this study, the LCA method and actual engineering data were used to carry out a detailed environmental impact assessment and decision-making on two remediation alternatives for a contaminated megasite in China. Subsequently, employing the input-output LCA (IO-LCA) method with Chinese local data and cost breakdown, a further environmental impact assessment of the selected alternative was performed. The findings revealed that Alt 1, which combined multiple remediation technologies, achieved a superior overall environmental impact score of 6.94 MPt compared to Alt 2, primarily based on landfill (score of 7.89 MPt). Despite the considerable uncertainty in the LCA results, Alt 1 had a 98.1% probability of outperforming Alt 2. In terms of the entire economic chain, the selected Alt 1 was estimated to emit 1.06 × 1010 kg CO2-eq, 6.06 × 106 kg of PM, 4.8 × 106 kg of NOx, 3.49 × 106 kg of SOx, 5.06 × 105 kg of COD, and 3.29 × 104 kg of NH3-N. The IO-LCA results indicated that LCA may substantially underestimated the environmental footprint of contaminated megasites remediation. This study provided valuable insight into the environmental assessment of contaminated megasites remediation and the selection and optimization of alternatives from a green and sustainable perspective.

Hybrid Life Cycle Assessment (LCA) of prefabrication: A comparison of conventional and mixed reality-based solutions

Conventional prefabrication in construction is causing distinct challenges, including environmental pollution and high energy usage. However, integrating emerging technologies such as Mixed Reality (MR) in prefabrication is expected to mitigate the environmental impacts and improve overall sustainability. This study evaluates and analyses the environmental benefits and burdens associated with implementing MR technologies in prefabrication compared to conventional prefabrication. The production stages in prefabrication, including reinforcement, side formworks, concreting, and human labor, have been identified as primary unit processes, and their respective contributions to the overall environmental impact have been assessed. A comprehensive assessment using a hybrid Input-Output Life Cycle Assessment (IO-LCA) model, while incorporating human labor hours is necessary for a thorough environmental assessment. The cradle-to-gate LCA results of a declared unit of 1 m3 volume of conventional prefabrication are compared with MR-enabled prefabrication. The results show that using MR technology in prefabrication significantly decreases the environmental burdens of the prefabrication in impact categories of Global warming (8%), Terrestrial acidification (8%), Marine eutrophication (10%), and Stratospheric ozone depletion (17%). Furthermore, with the higher levels of MR implementation, there is a significant reduction (around 60%) in the environmental impact associated with prefabrication. Accordingly, the contribution of MR technology in designing, planning, monitoring, assembling, and collaborating in prefabrication leads to effective resource management, minimization of waste, and enhancement of overall efficiency. The reinforcement process has notably elevated environmental impact in prefabrication while recognizing the improvement opportunities through MR implementation. MR-enabled quality inspection is of great importance in significantly reducing environmental burdens.

Evaluating Carbon Emissions during Slurry Shield Tunneling for Sustainable Management Utilizing a Hybrid Life-Cycle Assessment Approach

The construction sector is one of the principal contributors to carbon dioxide emissions (CDEs) and has a vital role to play in responding to the issue of long-term environmental sustainability. This research proposes a process-based hybrid life-cycle assessment (LCA) method depending on a process-based LCA and an input–output LCA. The process-based hybrid LCA model provides a supplementary method to quickly estimate carbon emissions that are not considered in the system boundary due to the limitation of inventory data. The proposed hybrid method was applied to a carbon emissions assessment in a slurry shield tunnel. The results suggest that 93.88% of emissions are from materials. Of the materials contribution, 55.9% comes from steel and 34.55% arises from concrete. It has also been found that emissions during the tunneling stage are negatively correlated with the efficiency of tunnel construction. Recommendations for carbon emissions reductions in tunnel construction are provided for promoting sustainable transportation and management.

Method for Calculating the Avoided Impact of Specific Information and Communication Technology Services

Particular Information and Communication Technology (ICT) services can help avoid environmental impact in larger contexts. However, there is no commonly agreed bottom-up methodology for calculation of the total net reduction effect of specific digital ICT services. Life Cycle Assessment (LCA) is a common denominator for most methodologies. The most common method is the Attributional LCA (ALCA), and recently the emerging handprint ALCA estimating so-called positive environmental impacts. Moreover, Consequential LCA (CLCA) can be used to capture market effects. The third conceptual approach is Input-Output LCA. The purpose is to propose and test a new method based on some of the existing ones. The existing concepts are compared and a synthesis is made to create a practical but still useful method. The new method is applied to two illustrative cases in the ICT domain; the introduction of a 5G enabled drone for pipe inspection and the 5G enabled health consultation. Compared to simplified ALCA, the difference between the absolute scores for the baseline system and the target system changes around 10% when the proposed simplified CLCA (SCLCA) method is used. The results show that SCLCA, when combined with analytical methods for expressing digital ICT services’ own impact, is a fruitful approach which is both practical and feasible. The new method includes formulae for calculating the total lifetime environmental impact of a specific ICT Equipment when reused or replaced.

Geospatial assessment of the carbon footprint of water and electricity consumption in residential buildings in Doha, Qatar

The process of estimating the carbon footprint (CF) has become a key method for managing greenhouse gas (GHG) emissions, guiding strategies for emission reduction and validating those strategies. Given the complexity of quantifying total lifecycle emissions in residential buildings, this study delves into assessing the CF focusing on water and electricity consumption in two types of residential buildings: mainly residential villas and residential flats. This assessment was carried out in Doha City, Qatar, using data on water and electricity consumption from 2017 to 2020. The study employs the Multi-Regional Input-Output Life Cycle Assessment (MRIO-LCA) model to calculate and convert the water and electricity consumption data into the CF of these buildings. Further, the study employs various methods for statistical and spatial statistical analysis of CF emissions, including geographically weighted regression (GWR), Ordinary Least Squares (OLS), and hotspot and cold spot assessments. The annual electricity CF emissions from these buildings are approximately 7 MtCO2 equivalent, with residential villas contributing about 83% of this total. Concurrently, the annual average water CF in residential buildings is around 0.06 MtCO2 equivalent, predominantly attributed to villas. The findings highlight the substantial impact of residential structures, particularly villas, on the city's overall CF emissions. Furthermore, the findings underscore the significant impact of residential structures, especially villas, on Doha's CF emissions, revealing a marked seasonal increase, particularly during the summer months and a notable spike in 2020. The spatial analysis of CF emissions reveals consistent spatial clustering patterns across different seasons in 2020. Elevated CF emissions from villas are concentrated in Doha's central, northern, and northeastern regions, while cold spots are predominantly in the eastern and southern areas. Understanding CF in residential settings is crucial for developing strategies to reduce emissions, enhance energy efficiency, and address climate change. This research helps inform targeted interventions for more sustainable residential energy and water use, aligning with broader environmental and climate goals.

Assessment of carbon footprint in Qatar's electricity sector: A comparative analysis across various building typologies

Carbon footprint (CF) estimation has emerged as an integral tool for greenhouse gas (GHG) management, providing direction for emission reduction strategies and verification processes. Since capturing the full lifecycle emissions of goods and services at the city level can be methodologically challenging due to the fact that cities have multiple sources of emissions (e.g. transportation, waste, buildings), our study focused on evaluating the carbon footprint of various types of urban buildings across six sectors. This evaluation was conducted in one of the most rapidly urbanized cities in the Middle East, Doha City, Qatar, and was based on electricity consumption data from 2017 to 2020. Using multiregional input–output life cycle assessment (MRIO-LCA) and hot/coldspot analysis, this study identified CF emission spatiotemporal patterns and the major buildings responsible for the highest emissions. The results indicated that residential villas and the commercial sector had the highest electricity consumption and steady increases in CF emissions during the study period, whereas hotels had the lowest annual electricity consumption among all sectors. The study also identified significant CF emission hotspots from Doha residential buildings in the southwestern and northeastern areas. In contrast, the CF hotspots from commercial buildings were concentrated in the industrial area in the southern part of the city and in the West Bay area in the eastern part. Overall, the findings of this study provide important policy implications and a comprehensive assessment of dynamic changes in CF emissions from different building types.

Structural Characteristics of the Household Carbon Footprint in an Aging Society

The aging population has posed a challenge to China’s carbon neutrality pledge. To study the household carbon footprint in an aging society, this paper has combined the age-specific consumption pattern and environmental input-output life cycle assessment (EIO-LCA) to calculate the carbon footprint of household consumption across age groups, and then identified the key pathways of carbon emissions via structural path analysis (SPA). Results indicate that the elderly contribute 11.65% to total consumption-based carbon emissions. The working group (ages 15–64) has the highest average carbon footprint (0.85 tCO2e), while the elderly group (ages 65 and above) has the lowest average carbon footprint (0.82 tCO2e). Urban households of all ages have a higher carbon footprint than rural households. Housing and food are the dominant sources of the elderly carbon footprint. Notably, the production and distribution of electric power and heat power sector associated with housing energy consumption plays a leading role in the carbon emissions pathways of elderly consumption. Measuring the carbon footprint of older people can support policy designs and decision making in key sectors along the supply chain, and further encourage low-carbon lifestyles among China’s elderly. Additionally, the findings of this study have broad applications, especially for developing countries undergoing demographic transitions.

Investigation of greenhouse gas emissions from municipal solid waste management practices using an economic input-output life cycle analysis approach

The urban solid waste management is gaining attention due to ecological, economic, and demographic factors, emphasizing the importance of sustainable practices and infrastructure development. There are two main objectives of this work: developing an effective framework to predict the environmental impacts of alternative packaging waste collection rates and estimating the associated environmental emissions, energy consumption, and the materials required for goods and services for the case of Avcilar Municipality in Istanbul City, Türkiye. To achieve these objectives, an Economic Input-Output Life Cycle Assessment model was implemented to assess the impacts of all associated industrial sectors. The results showed that as the amount of packaging waste collection increases, the top two sectors that are responsible for the most emissions are Oil and Gasoline Production and Electricity Production and Supply sectors. In addition, the economic analysis results of the study show that as the packaging waste collection rate increases, the unit packaging waste collection cost decreases. The findings of this study provide valuable insights into the environmental emissions of packaging waste management strategies. Moreover, this study highlights the importance of packaging waste management in reducing overall greenhouse gas emissions and promoting more sustainable waste management practices. Policymakers and waste management stakeholders can utilize this framework to identify strategies that minimize environmental impacts and carbon footprints, thereby fostering a transition towards more sustainable waste management options.

Carbon footprint of residents' online consumption in China

Although China boasts the largest online consumption market, there has been no prior case study addressing the carbon footprint of online consumption. The rapid expansion of online consumption has posed a challenge to China's carbon neutrality pledge. This paper aims to fill this crucial gap by conducting a pioneering study based on survey data collected from 6000 households. To achieve this goal, this study has combined provincial Multi-Regional Input-Output (MRIO) tables with residents' online consumption calculated by the Non-Linear Optimization model, and applied the Environmental Input-Output Life Cycle Assessment (EIO-LCA) model to calculate the carbon footprint of residents' online consumption. Result indicated that the average carbon footprint of residents' online consumption was 0.46 tCO2e in 2016, accounting for 12.30% of the total consumption carbon footprint. Notably, high-emission regions include Beijing-Tianjin, Jiangsu-Zhejiang-Shanghai, and Hubei. Moreover, the dominant sources of the online consumption carbon footprint were electricity, food, clothing, and beauty products. Furthermore, we found that the carbon footprint of online consumption tends to increase with income, with differential income elasticities observed for various products and income groups. We also quantified the average expenditure-based life-cycle carbon coefficient for parcel delivery (0.22 kgCO2e/yuan) and conducted two scenario analyses to assess its impact. From a policy perspective, our research on measuring the carbon footprint of residents' online consumption can provide valuable insights for designing future policies and decisions related to e-commerce and key sectors along the supply chains. Additionally, it will further encourage Chinese residents to adopt low-carbon lifestyles.

Multi-region Input-Output-based Carbon and Energy Footprint Analysis of U.S. Manufacturing

In this research, U.S. manufacturing activities' life cycle-based carbon and energy footprint impacts have been quantified, taking international trade linkages with the rest of the world into account. The U.S economy has been integrated into a multi-region input-output (MRIO) life cycle assessment framework where total of 40 major economies, including the USA, China, Russia, and others, plus the rest of the world (ROW) were modelled to assess global energy and carbon footprint impacts. Each country's economy is assumed to compromise 35 major industries based on the WIOD database classification. A total of 1435 (41 × 35 = 1435) industries has therefore been taken to represent the global structure of the world economy. The novelty of the approach is that the MRIO model has been developed in a stochastic fashion, plus global trade-linked uncertainties have also been taken into consideration. Top carbon emitting and energy consumer industries and countries have been analysed using data analytics and statistical modelling methods. The results show that the USA is the largest contributor to the total carbon footprint (CFP) and the total energy footprint (EFP) with 81.73% and 84%, respectively. Moreover, the agriculture/hunting forestry/fishing sector and the electricity/gas/water supply sectors dominate the overall U.S. carbon footprint, contributing 22% and 21.28%, respectively. The coke/refined petroleum/nuclear fuel sector has the largest share of the total energy footprint, with 47.9% of the total impacts.

Are SRI funds financing carbon emissions? An input-output life cycle assessment of investment funds

Indirect greenhouse gas (GHG) emissions (scope 3) generally represent more than half of the total life cycle impact attributable to a company or an investment. However, widely used sustainability assessment tools for investment funds fail to take these into account. Building on best practices from the industrial ecology field, we develop an input-output life cycle assessment (IOLCA) methodology to estimate life cycle GHG emissions of companies and investment funds. We apply our method to a sample of 1340 sustainable (SRI) and conventional equity funds domiciled in Europe and their 11,275 unique holdings. We extend our application to a case study of funds self-classified as Article 8 and Article 9 funds under the recent European Sustainable Finance Disclosure Regulation (SFDR, 2019). Our model estimates life cycle emissions for 94% of the companies held – compared to 17% coverage in the Carbon Disclosure Project (CDP). When including scope 3, the exposure to GHG emissions of both SRI and conventional funds is two to three times larger than when considering only direct impacts from holdings' operations. Finally, 24% of the sampled Europe-domiciled SRI funds are more exposed to life cycle carbon emissions than the ETF tracking the conventional market index MSCI Europe.

Higher adherence to the Dietary Approaches to Stop Hypertension (DASH Diet) is associated with lower greenhouse gases and land use from protein foods

IntroductionThe average American diet is high in red and processed meats which increases one's risk for chronic diseases and requires more land and water to produce and yields greater greenhouse gases (GHG) compared to other protein foods. Reduction of red and processed meat intake, such as seen with the Dietary Approaches to Stop Hypertension (DASH diet), could benefit human and environmental health.ObjectiveThe objective of this study is to predict the environmental sustainability of the DASH diet by evaluating the GHG, land use, and water withdrawals from protein foods within the self-selected diets of people who were encouraged to follow the DASH diet.MethodsDietary data was collected from 380 Midwesterners aged 35-70 years old with hypertension using the Automated Self-Administered 24-Hour (ASA 24) Recall System. DASH diet adherence was measured using a nutrient-based DASH score. GHG, land use, and water withdrawals were obtained using Carnegie Mellon University's Economic Input-Output Life Cycle Assessment (eiolca.net) using the Purchaser model (cradle-to-consumer). Multiple linear regressions were used to view associations between individual DASH nutrient scores and environmental impacts of total, animal, and plant protein foods.ResultsDiets that met DASH diet guidelines, as indicated by higher individual DASH nutrient scores, were associated with less GHG and land use from total and animal protein foods but more GHG and land use from plant-protein foods, with a few exceptions. The pattern was not clear for water withdrawals. Diets with the greatest adherence had around 25–50% lower GHG and land use from total protein foods than diets with the lowest adherence. Changes may be due to decreased consumption of total and animal protein foods, selection of animal protein foods with lower environmental impacts, and increased consumption of plant protein foods.ConclusionAdhering to the DASH diet can promote the consumption of less environmentally demanding protein foods resulting in lower GHG and land use from protein foods. However, claims regarding the sustainability of the entire dietary pattern cannot be determined based off the current study. Regardless, it is evident that environmental impacts should be considered alongside health impacts when selecting, promoting, or recommending a dietary pattern.

Macro-economic impacts of renewable energy transition in India: An input-output LCA approach

India's higher emphasis on Renewable Energy adoption is aligned with the twin Sustainable Development Goals of tackling climate change (Goal 13) and ensuring sustainable, affordable, reliable energy for all (Goal 7). This study intends to evaluate the macroeconomic impact of India's ambitious 280 GW solar and 140 GW wind capacity expansion programme by 2030 using an Input-Output framework. The economic implications of solid waste generation and waste abatement in the End-of-life phase are evaluated. The solar and wind energy's economy-wide contribution is minimal; however, the solid waste generation is significant, ranging between 7.1–24 thousand tonnes of PV waste and 14.2–48.3 million tonnes of wind turbine waste in different solar and wind capacity mix. The solid waste abatement cost which constitutes the treatment of materials is aimed towards the attainment of the 3R strategy. The Green-GDP estimate by accounting for the net-environmental and health burden of solar and wind energy mix and the material waste generation witnesses 0.9 % to 1.52 % increase in net loss to GDP in different scenarios. The substantial emission savings of 11–23 % from the transition towards solar and wind energy can serve as a catalyst in helping India achieve its decarbonization targets.

Performance-based seismic resilience and sustainability assessment of coastal RC bridges in aggressive marine environments

Corrosion-induced deterioration of material properties can noticeably affect the seismic performance of coastal reinforced concrete (RC) bridges in aggressive marine environments, causing significant adverse economic and environmental consequences post extreme events. This study systematically investigates the time-dependent seismic performance, resilience, and sustainability of coastal RC bridges. For that purpose, a three-dimensional (3D) nonlinear finite element (FE) framework is developed and combined with the formulation of performance-based earthquake engineering (PBEE) and an economic input-output life cycle assessment (LCA) approach. Seismic resilience and robustness of the coastal RC bridges are subsequently evaluated in terms of the post-earthquake repair cost, repair time, and carbon footprint quantified by CO2. Within this framework, additional analyses are conducted to explore possible retrofit measures, including the use of fiber-reinforced polymer (FRP) reinforcement on the sustainability and resilience of the retrofitted RC bridges. For the presented scenario, it is shown that wrapping bridge columns with FRP composites can effectively increase the moment capacity, thus improving resilience and sustainability by reducing as much as 650 Mg of CO2 for this specific bridge. Overall, the derived insights highlight the need for sustainability, resilience, and potential retrofit analysis of coastal RC bridges in seismically active regions affected by corrosion-induced deterioration.

A Review on Economic Input-Output Analysis in the Environmental Assessment of Electricity Generation

This paper aims to review one of the least used, but no less important, approaches in the assessment of the environmental implications of electricity generation: the Economic Input-Output Life Cycle Assessment (EIO-LCA). This methodology is a top-down approach intertwined with the environmental satellite accounts provided by the national statistical office. Through the use of economic input-output (IO) tables and industrial sector-level environmental and energy data, the EIO-LCA analysis allows for broad impact coverage of all sectors directly and indirectly involved with electricity generation. In this study, a brief overview of this methodology and the corresponding assumptions is presented, as well as an updated review of the different applications of the EIO-LCA approach in electricity generation, suggesting a possible classification of the many studies developed in this context. The different ways of overcoming the problem of disaggregation in the electricity sector are also addressed, namely by considering different IO table formats (i.e., symmetric or rectangular tables). This is a particularly relevant feature of our review, as the way in which electricity generation is modeled can result in different calculations of the costs and benefits of environmental policies. In this context, this paper further contributes to the literature by explaining and providing examples of distinct approaches to modeling the electricity sector in IO models on a detailed level.

Driving forces and variation in water footprint before and after the COVID-19 lockdown in Fujian Province of China

The COVID-19 outbreak has injured the global industrial supply chain, especially China as the world's largest manufacturing base. Since 2020, China has implemented a rigorous lockdown policy, which has sternly damaged sectoral trade in export-oriented coastal areas. Fujian Province, which mainly processes imported materials, has a more profound influence. Although the COVID-19 lockdown has had some detrimental consequences on the world economy, it also had some favorable benefits on the global ecology. Previous studies have shown that the lockdown has altered the physical water quantity and quality, but the lack of total, virtual, and physical water research that combines water quantity and water quality simultaneously to pinpoint the subject and responsibility of water resources consumption and pollution. This research quantified the physical, virtual, and total water consumption and water pollution among 30 sectors in Fujian Province based on the theory of water footprint and the Economic Input-Output Life Cycle Assessment model. SDA model was then used to investigate the socioeconomic elements that underpin variations in the water footprint. The results show that after the lockdown, the physical water quantity and the physical grey WF in Fujian Province decreased by 2.6 Gm3 (−6.7%) and 0.4 Gm3 (−1.3%) respectively. The virtual water quantity decreased by 2.3 Gm3 (−4.5%), whereas the virtual grey WF rose by 1.5 Gm3 (4.3%). The total water quantity dropped by 3.3 Gm3 (−4.9%), while the grey WF increased by 1.2 Gm3 (2.5%), i.e. the COVID-19 lockdown decreases physical water quantity and improves local water quality. More than 50% of the water comes from virtual water trade outside the province (virtual water is highly dependent on external), and around 60% of the grey WF comes from physical sewage in the province. The COVID-19 lockdown reduced water outsourcing across the province (paid nonlocally decrease) but increased pollution outsourcing (paid nonlocally increase). And gross capital formation's contribution to the growth in water footprint will continue to rise. As a result, this study suggested that Fujian should take advantage of sectoral trade network to enhance the transaction of green water-intensive intermediate products, reduce the physical water consumption of blue water-intensive sectors, and reduce the external dependence on water consumption. Achieving the shared responsibility of upstream and downstream water consumption and reducing the external dependence on water in water-rich regions is crucial to solving the world's water problems. This research provides empirical evidence for the long-term effects of COVID-19 lockdown on the physical and virtual water environment.

Potential for reducing CO2 emissions from passenger cars in Japan by 2030 to achieve carbon neutrality

For Japan to achieve its targets for carbon neutrality and reduction of carbon dioxide (CO2) emissions, decarbonization of road transport is essential. Japan regards next-generation vehicles, including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell vehicles, as a key means of reducing carbon emissions from road transport. Four scenarios were proposed to predict the potential carbon emissions reduction of passenger car use in 2030: baseline, business-as-usual, government's target-based, and aggressive scenarios. Economic input–output life cycle assessment was used to evaluate potential CO2 emissions, acidification, eutrophication, human toxicity, and photochemical oxidation associated with passenger cars. In this study, all environmental impacts were calculated using the multi-regional environmentally extended supply and use/input–output database by applying the CML 2001 impact assessment method. The findings indicate that PHEVs have the lowest CO2 emissions per km traveled, followed by HEVs and BEVs. The prediction for carbon emissions from passenger cars shows that adopting electrified vehicles, such as HEVs, PHEVs, and BEVs could help decarbonize the passenger car sector. The population of vehicles, vehicle manufacturing, well-to-wheel cycle of fuel, and fuel economy will significantly contribute to CO2 emissions. Finally, this study recommends policies to steer Japan into achieving its goal of carbon neutrality.

Study on Embodied CO2 Emissions and Transfer Pathways of Chinese Industries

Industries with low direct CO2 emissions downstream in the industry chain have significant carbon emissions upstream, which is similar to how carbon leakage in interprovincial regions and international commerce affects these regions. Due to the interchange and transit of goods, there are intermediate production and consumption processes across industrial sectors. The CO2 emissions produced by each sector are insufficient to satisfy the sector’s ultimate demand. It will also move along with the industrial chain. Investigating embodied carbon transfer across industrial sectors is crucial to strike a balance between economic growth and greenhouse gas emissions. Locating the key sectors to reduce carbon emissions provides a basis for formulating resource conservation and environmental protection policies. In this study, the industrial sector divides into 24 subsectors, and the embodied CO2 emissions and carbon transfer pathways of each are examined from the viewpoint of the industrial chain using the Economic Input–Output Life Cycle Assessment (EIO-LCA) and the Hypothetical Extraction Method (HEM). The indirect CO2 emissions downstream of the industrial chain are higher than the direct carbon dioxide emissions, and the intersectoral carbon transfer constitutes a significant part of the total carbon emissions of the industrial sector. The upstream sector of the industry chain has a significantly higher direct carbon emission intensity than the indirect CO2 emission intensity, while the downstream sector is the opposite. The production and supply of electricity, gas and water, and raw material industries transfer significant CO2 to other sectors. The manufacturing industry is mainly the inflow of CO2. CO2 flows from the mining industry to the raw material industry and from the raw material industry to the manufacturing industry constitute the critical pathway of carbon transfer between industries. A study on the embodied carbon emissions and transfer paths of various industrial sectors is conducive to clarifying the emission reduction responsibilities and providing a basis for synergistic emission reduction strategies.

Assessing the Impact of the Recent Unprecedented World Events on the Economic and Environmental Conditions of Saudi Arabia

This study quantitatively analyses the impacts that recent unprecedent events have had on the Saudi economy and environment using the Global Trade Analysis Project (GTAP) model. These events include: the global outbreak of COVID-19 and the associated disruption to the global supply chain, the alarming rate of climate change, and various political conflicts. These events have affected global food and energy prices. The results of this study revealed a decline in Saudi GDP, household income, purchase ability, and welfare. A trade deficit was indicated in the Saudi trade balance because of higher food prices and a reduction in two of the main Saudi exports (oil and petroleum products). A decrease in the output of most Saudi industries was shown, despite the increase in exports for most sectors. This was because of the reduction in Saudi households’ domestic consumption. Regarding the environmental impact, the Input–Output Life Cycle Assessment (IO-LCA) approach was used to estimate the total CO2 emissions of the Saudi economy. In total, approximately 740.6 million metric tons of CO2 emissions were estimated. By using a recently published specific carbon intensity for Saudi oil, total Saudi CO2 emissions were 24.59% less than the non-specific measure.