Input-output Life Cycle Assessment Model Research Articles

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.

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.

Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach

AbstractThe formation of an industrial network is inevitable because an industrial structure evolves to a higher level. The continuous material exchange among sectors creates a path for the embodied carbon in products to flow freely among industries. With the gradual complication of industrial networks, however, the emission reduction behavior of sectors not only becomes rooted in individual conduct, but also originates from the interaction forces between individuals. Therefore, carbon management in industries in the real economy needs to be explored from the perspective of these complex networks. On the basis of these considerations, this study utilized a hybrid model, the Economic Input–Output Life Cycle Assessment model, to construct the carbon footprint network of China's industries in 2002, 2007, 2012, and 2017. We also applied social network analysis to quantify the structural characteristics and evolutionary patterns of the network. Results showed that the transmission efficiency of resources and the subgroup‐integrated degree of the actors within the carbon footprint network were excellent, which was conducive to coordinated carbon emission reduction of these sectors from a network perspective. Agriculture and services were the engines driving sector‐wide carbon emission reduction through links within the network. Furthermore, some sectors, such as transportation and the warehousing industry, basic chemicals and chemical products manufacturing, and metal smelting and rolling processing industry, act as bridges and brokers. These sectors are keys to ensuring network cohesion and reducing the risk of network fragmentation. Finally, the industrial block network presented a hierarchical structure of a core–edge network. The network followed a trend of structural changes in which the network core became increasingly clear, and the network distribution pattern gradually concentrated on a single block. This finding revealed a new pathway for collaborative carbon mitigation in the industry from the perspective of the industrial community.

Identifying data gaps in the energy supply chains of manufacturing sectors with an input–output LCA model

Abstract National Renewable Energy Laboratory's Materials Flows through Industry tool was developed to analyze energy consumption across the supply chains of U.S. manufacturing industries. Due to the limitation of the data sources, the Tool may have incomplete data in some industries’ supply chains. To overcome this issue, this study provides a method to identify data gaps in the MFI tool with the information in input–output life cycle assessment models. Results show that around 5% processes in the MFI tool have potential data gaps in the supply chain. LCA practitioners can use this information to optimize activities to improve LCA models.

Sectoral changing patterns of China’s green GDP considering climate change: An investigation based on the economic input-output life cycle assessment model

Abstract The rapid growth of energy-intensive and high-emission industries has underpinned China’s economic boom over the past few decades. Since her economic development entered the new normal phase in 2013, China has faced the challenge of mitigating greenhouse gas (GHG) emissions while achieving medium-high economic growth. Transforming the economic development pattern and restructuring the economy is a principal solution, and one important prerequisite is discerning great sectoral disparities of GHG emissions and corresponding environmental costs because the diversity of characteristics among different sectors causes the pollutants that are discharged to vary. Hence, this paper aims to assess the environmental costs of China’s total and sectoral GHG emissions. Based on the System of Environmental and Economic Accounts and using the economic input-output life cycle assessment model, this study calculates the green GDP and green output value of 27 sectors to reflect the environmental costs of GHG emissions in China during 1991–2016. The findings are as follows: (1) while China’s direct GHG emissions increased from 3,040.60 million tons of carbon dioxide equivalent (MtCO2eq) to 10,641.10 MtCO2eq during 1991–2014, declining trends were observed in the total and 16 sectors’ direct GHG emissions in the subsequent two years; (2) although the ratios of direct GHG emissions to total GHG emissions in most sectors decreased, total GHG emissions in eight sectors rose first and then fell, and in 15 sectors continued to rise; (3) China’s green GDP grew from 2,003.88 billion Chinese Yuan to 26,245.25 billion Chinese Yuan during 1991–2016, and the difference between China’s GDP and green GDP decreased from 2.73% to 1.02%; and (4) differences between the output value and green output value decreased in over 20 sectors. Finally, some policy implications are given from the perspective of some key sectors of the Manufacturing industry, Agricultural sector, and Transport, storage, and post sector.

Carbon footprint accounts of Pakistan: an input-output life cycle assessment model.

The Paris agreement (2015) seems a significant achievement towards a global mitigation policy to climate change. However, implementing the promised Intended Nationally Determined Contribution (INDC) targets by the participating countries has become a real challenge. In this aspect, the input-output life cycle assessment (IO-LCA) model provides an important assessment mechanism to design suitable abatement policies limiting the rising greenhouse gas (GHG) emissions. The present paper develops an IO-LCA model for Pakistan and estimates all the direct and indirect GHG emissions caused by all the production activities during all the stages of production. This task is achieved in three phases. In phase 1, the Pakistan input-output table (IOT) is constructed. In phase 2, the GHG environmental satellite accounts are created for each sector in the economy. In phase 3, the GHG emissions are linked to different categories of final demand.

Multi-sector reduction potential of embodied carbon emissions in China: a case study of Liaoning province

This study indicates that energy-related industries, such as coal mining and dressing, metal melting and rolling, electric and heat power, as well as petroleum refining, are the central causes of carbon emissions. China has been the largest emitter of carbon dioxide worldwide since 2008, and its current energy structure is still coal-dominated, which further increases carbon emissions. The reduction in these carbon emissions is a focus of attention in China, and the carbon reduction potential is particularly studied with the aim to issue appropriate mitigation policies. This study calculates the total embodied carbon emissions from a multi-sector perspective, using the economic input–output life cycle assessment model and analyzing the carbon reduction potential of Liaoning province, China. The level of export and provincial outflow consumption is the primary cause that leads to embodied carbon emissions from the consumption demand perspective. The carbon reduction potential is divided into three grades (weak, medium, and strong). Energy-intensive industries were classified as high carbon emission industries with the strongest potential for carbon emission reduction. This study provides policy insights that will aid the preparation of emissions reduction strategies.

Greenhouse gas emissions, total food spending and diet quality by share of household food spending on red meat: results from a nationally representative sample of US households.

To determine if US household food purchases with lower levels of red meat spending generate lower life-cycle greenhouse gas emissions (GHGE), greater nutritional quality and improved alignment with the Dietary Guidelines for Americans. Affordability of purchasing patterns by red meat spending levels was also assessed. Household food purchase and acquisition data were linked to an environmentally extended input-output life-cycle assessment model to calculate food GHGE. Households (n 4706) were assigned to quintiles by the share of weekly food spending on red meat. Average weekly kilojoule-adjusted GHGE, total food spending, nutrients purchased and 2010 Healthy Eating Index (HEI-2010) were evaluated using ANOVA and linear regression. USA.ParticipantsHouseholds participating in the 2012-2013 National Household Food Acquisition and Purchase Survey. There was substantial variation in the share of the household food budget spent on red meat and total spending on red meat. The association between red meat spending share and total food spending was mixed. Lower red meat spending share was mostly advantageous from a nutritional perspective. Average GHGE were significantly lower and HEI-2010 scores were significantly higher for households spending the least on red meat as a share of total food spending. Only very low levels of red meat spending as a share of total food spending had advantages for food affordability, lower GHGE, nutrients purchased and diet quality. Further studies assessing changes in GHGE and other environmental burdens, using more sophisticated analytical techniques and accounting for substitution towards non-red meat animal proteins, are needed.

Representing and visualizing data uncertainty in input-output life cycle assessment models

Uncertainty assessment is crucial to the reliability of decisions made based on results of life cycle assessment (LCA) models. However, the most popular uncertainty analysis method in LCA models defines uncertainty as quantified empirical judgment of the sources of the inventory data, which lacks the deviations due to the measurements of the data. We create a modified range method that leverages an array of publicly available data to represent the uncertainty of the actual values used in input-output models as ranges. Using these ranges, we propagate and visualize results from the uncertainties. We demonstrate the utility of the method using the Economic Input-Output Life Cycle Assessment (EIO-LCA) model and focus on the uncertainty of estimated energy consumption. Results show that for energy consumption values in the model, average uncertainty ranges are within ±40% with some outliers. Our method screens based on the magnitude of impacts and the relative uncertainty. Improved uncertainty assessment supports various types of decisions, such as product comparisons, hotspot analysis, and overall energy analyses. We used three case studies to demonstrate the implementations of our method. This method can be extended to additional types of flows, beyond energy, and to process-matrix-based LCA models.

An extended environmental input–output lifecycle assessment model to study the urban food–energy–water nexus

We developed a physically-based environmental account of US food production systems and integrated these data into the environmental-input–output life cycle assessment (EIO-LCA) model. The extended model was used to characterize the food, energy, and water (FEW) intensities of every US economic sector. The model was then applied to every Bureau of Economic Analysis metropolitan statistical area (MSA) to determine their FEW usages.The extended EIO-LCA model can determine the water resource use (kGal), energy resource use (TJ), and food resource use in units of mass (kg) or energy content (kcal) of any economic activity within the United States. We analyzed every economic sector to determine its FEW intensities per dollar of economic output. This data was applied to each of the 382 MSAs to determine their total and per dollar of GDP FEW usages by allocating MSA economic production to the corresponding FEW intensities of US economic sectors. Additionally, a longitudinal study was performed for the Los Angeles–Long Beach–Anaheim, CA, metropolitan statistical area to examine trends from this singular MSA and compare it to the overall results.Results show a strong correlation between GDP and energy use, and between food and water use across MSAs. There is also a correlation between GDP and greenhouse gas emissions. The longitudinal study indicates that these correlations can shift alongside a shifting industrial composition. Comparing MSAs on a per GDP basis reveals that central and southern California tend to be more resource intensive than many other parts of the country, while much of Florida has abnormally low resource requirements.Results of this study enable a more complete understanding of food, energy, and water as key ingredients to a functioning economy. With the addition of the food data to the EIO-LCA framework, researchers will be able to better study the food–energy–water nexus and gain insight into how these three vital resources are interconnected. Applying this extended model to MSAs has demonstrated that all three resources are important to a MSA’s vitality, though the exact proportion of each resource may differ across urban areas.

Carbon footprint stock analysis of US manufacturing: a time series input-output LCA

PurposeThe purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries.Design/methodology/approachInitially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO2-equivalent, whereas the economic outputs were measured in million dollar economic activity.FindingsThe salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO2 intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.”Originality/valueThis paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

The state of supply chain carbon footprinting: analysis of CDP disclosures by US firms

Although collaboration and performance measurement are widely recognized as critical for increasing sustainability in supply chains, little is known about how comprehensively firms are currently measuring their supply chain carbon emissions (or Scope 3). We develop a way to assess how complete firms' reports of Scope 3 emissions are, by comparing them to benchmarks based on Environmental Input-Output Life-Cycle Assessment models. We use this approach to evaluate Scope 3 carbon emissions disclosed by many of the largest firms in the United States to CDP (formerly the Carbon Disclosure Project). We estimate that, on average, these firms reported 22% of their full Scope 3 emissions in 2013. Our results show that Scope 3 reporting varies widely across industries. We highlight two firms that are able to capture a significant portion of their Scope 3 carbon emissions despite having a large number of suppliers. Although firms are beginning to account for an increasing portion of their supply chain's carbon emissions, there is substantial room for improvement in their measurement and disclosure of Scope 3 emissions.

A fuzzy data envelopment analysis framework for dealing with uncertainty impacts of input–output life cycle assessment models on eco-efficiency assessment

The uncertainty in the results of input–output-based life cycle assessment models makes the sustainability performance assessment and ranking a challenging task. Therefore, introducing a new approach, fuzzy data envelopment analysis, is critical; since such a method could make it possible to integrate the uncertainty in the results of the life cycle assessment models into the decision-making for sustainability benchmarking and ranking. In this paper, a fuzzy data envelopment analysis model was coupled with an input–output-based life cycle assessment approach to perform the sustainability performance assessment of the 33 food manufacturing sectors in the United States. Seven environmental impact categories were considered the inputs and the total production amounts were identified as the output category, where each food manufacturing sector was considered a decision-making unit. To apply the proposed approach, the life cycle assessment results were formulated as fuzzy crisp valued-intervals and integrated with fuzzy data envelopment analysis model, thus, sustainability performance indices were quantified. Results indicated that majority (31 out of 33) of the food manufacturing sectors were not found to be efficient, where the overall sustainability performance scores ranged between 0.21 and 1.00 (efficient), and the average sustainability performance was found to be 0.66. To validate the current study's findings, a comparative analysis with the results of a previous work was also performed. The major contribution of the proposed framework is that the effects of uncertainty associated with input–output-based life cycle assessment approaches can be successfully tackled with the proposed Fuzzy DEA framework which can have a great area of application in research and business organizations that use with eco-efficiency as a sustainability performance metric.

An Estimation of the Evolution of Waste Generated by Direct and Indirect Suppliers of the Spanish Paper Industry

The generation of waste by the paper industry has attracted great attention over the last decades among other reasons because the demand for recycled waste paper has considerably increased. As the paper industry is closely intertwined to the rest of industries in the production system, its activity exerts both a direct and indirect influence on the volume of waste generated by its supplier industries. The purpose of this study is to shed some light on the evolution of the volume of waste generated by the suppliers of the Spanish paper industry over the period 2005–2010 using an Economic Input–Output Life Cycle Assessment Model. In particular we focus on the evolution of the volume of waste generated by firms of the own paper industry. We employ data from different waste surveys conducted by the Spanish National Statistics Institute and input–output tables extracted from the World Input–Output Database. The results obtained show that the waste generated by suppliers amounted to 1250 thousand tonnes in 2010, an important volume if we take into account that the waste generated by the paper industry in 2010 amounted to 1739 thousand tonnes. The analysis of the evolution of the waste generated by suppliers reveals that there is a high degree of concentration, both in terms of industries and in terms of waste categories. In addition, the decrease in the volume of waste generated by supplier firms within the own paper industry reflects not only the growing importance of recycled paper as raw material for paper-making but also the important investments in technology made by this industry in Spain.

Economic and environmental analysis of five Chinese rural toilet technologies based on the economic input–output life cycle assessment

Building toilets are critical infrastructure to promote public health. The current sanitation technology in developed countries is based on diluting human excreta with large volumes of centrally provided potable water. This approach is a poor use of water resources and is also inefficient and energy intensive, which is not suitable for water-shortage rural areas of northern China. Therefore, comprehensive schemes of Chinese rural toilets, including standard flushing, rainwater harvesting flushing systems, urine separation and composting systems, were considered in this study by means of five scenarios simulation. Economic net present value method was adopted for economic analysis and economic input–output life cycle assessment model based on Chinese economic input–output table and life cycle database was developed for comparing energy consumption and greenhouse gas emission implications, aiming to guide the selection of toilet schemes. A mountainous village in Mentougou District of Beijing was taken as the example. Based on total cost and net present value method, the results showed that scenario 5, namely urine diverting composting systems, outweighed the other four alternatives on the cost even in the different discount rates ranging from 0% to 10%; scenario 3, namely urine-diversion and potable water flushing toilet, was sub-optimal economic solution which net present value was negative when discount rate was over 8%. This study also present scenario 3's environmental superiority to scenario 5 on energy consumption and greenhouse gas emission. These two technologies were proved viable options for standard flushing toilet. Nevertheless, for another two scenarios of rainwater harvesting flushing toilets, they had higher amounts of investment, energy consumption and greenhouse gas emission due to the high investment on rainwater tank construction. Moreover, the vast majority of construction materials required high energy consumption and were carbon intensive sectors in China. Finally, some suggestions were given for the implementation of urine separation toilets and composting toilets in the practical application. Meanwhile, impact of the rising water price in selecting alternative technologies and tradeoff about the priority consideration for rural toilet technologies were analyzed in this study. Considering the great significance of rainwater harvesting toilet technology to save the water resources, measures of how much government should subsidize for guarantee its extension and motivation was discussed as well.

The carbon implications of declining household scale economies

In the United States, average household size decreased significantly over the past half century. From 1950 to 2010, the number of households increased 72% faster than population size. In this paper we consider how this drift toward more and smaller households, occurring alongside rising affluence, undermines efforts to curb carbon dioxide (CO2) emissions by eroding household scale economies of consumption and associated CO2 emissions. To estimate the household scaling of CO2 emissions, we link consumer expenditure data to an economic input–output life-cycle assessment model. We find that the CO2 scaling benefits of cohabitation are compellingly large, with the carbon footprint of a representative person cohabiting with others being 23% less, on average, than if that same person lived alone. Additionally, we find that household scale economies: 1) decrease in income, reflecting the rise in the percentage of household expenditures devoted to more rival goods and services; and 2) increase intuitively in household size, reflecting the direct expenditure sharing benefits of cohabitation. The combined downward pressure on scale economies from declining household size and rising incomes, typifying the trajectory of developing societies toward more and smaller households and rising affluence, places significant upward pressure on CO2 emissions globally.

Understanding the shadow impacts of investment and divestment decisions: Adapting economic input–output models to calculate biophysical factors of financial returns

In recognition of the cumulative effects resulting from financial decisions, a growing number of campaigns are advocating for the removal of investment funds from companies responsible for high levels of carbon emissions. A systematic approach can aid in examining the social, economic and environmental impacts that extend beyond political motivations to divest from fossil fuel companies.We have adapted publicly available economic input–output life cycle assessment models (EIO-LCA) to develop a Shadow Impact Calculator (SIC) for examining the potential environmental impacts of investment decisions. An investment portfolio's shadow impacts represent the economic, social and environmental effects underlying an investor's decision to place their funds in particular financial instruments. In this study, we focus on greenhouse gas emissions to show which sectors of the United States economy have particularly large or small carbon shadows and place those results in the context of volatility and earnings. To demonstrate how SIC may be used, we examine the endowment investments of a Canadian university in the context of divesting from fossil fuel companies. Our analysis suggests that large pooled funds choosing to direct their investments away from heavy carbon emitters may have less of an impact than would otherwise be expected.

Unintended Environmental Consequences and Co-benefits of Economic Restructuring

Current economic restructuring policies have ignored unintended environmental consequences and cobenefits, the understanding of which can provide foundations for effective policy decisions for green economy transformation. Using the input-output life cycle assessment model and taking China as an example, we find that household consumption, fixed capital formation, and export are main drivers to China's environmental impacts. At the product scale, major contributors to environmental impacts vary across different types of impacts. Stimulating the development of seven strategic emerging industries will cause unintended consequences, such as increasing nonferrous metal ore usage, terrestrial acidification, photochemical oxidant formation, human toxicity, and terrestrial ecotoxicity. Limiting the surplus outputs in the construction materials industry and metallurgy industry may only help mitigate some of the environmental impacts caused by China's regulated pollutants, with little effect on reducing other impacts, such as marine eutrophication, terrestrial acidification, photochemical oxidant formation, and particulate matter formation. However, it will bring cobenefits by simultaneously reducing mineral ore usage, human toxicity, marine ecotoxicity, and terrestrial ecotoxicity. Sustainable materials management and integrated policy modeling are possible ways for policy-making to avoid unintended consequences and effectively utilize cobenefits.

Waste management of urban agglomeration on a life cycle basis

Recent material metabolism research has been conducted into urban and regional area, but relatively little research explores material metabolism relationships within urban agglomerations. Therefore, this study attempts to explore and analyze one category of material metabolism in an urban agglomeration. Furthermore, it provided a scientific basis for waste management within urban agglomerations. It did this by changing the recycling mode of material metabolism and investigated the resulting life cycle effects. The Chinese urban agglomeration of Su–Xi–Chang was taken as a case study. A mixed-unit input–output life cycle assessment model was used to analyze scrap tire management. The results show that China's energy savings and emission reduction requirements are best met by centralized recycling. However, in promoting centralized recycling, attention should be paid to improving production technologies so as to reduce negative resources and environmental impacts. This paper's analysis can be applied equally to resources management and the evaluation of other waste flows in urban agglomerations.

Unintended consequences of bioethanol feedstock choice in China

Economic, energy, and environmental impacts of 11 types of bioethanol feedstock in China were evaluated using a mixed-unit input–output life cycle assessment model. Corn grain and wheat grain had higher negative economic, energy, and environmental impacts. Sweet sorghum, cassava, sugar beet, and sugarcane showed better economic performance but increasing negative energy and environmental impacts. Cellulose-based feedstocks in general showed positive economic, energy, and environmental performance; but may lead to increasing negative impacts on freshwater use, global warming, toxicity, and aquatic ecotoxicity. Sugarcane-based bioethanol had the potential to provide positive economic, energy, and environmental impacts in China. Scrap paper-derived ethanol could also become promising under significant government support.