AbstractInnovative recycling technologies can help curb food waste, yet their implementation often involves trade-offs among different environmental issues and among environmental, economic and social issues. Monetization can provide a solution to integrate all environmental impacts across the life cycle of food waste and to enable a normalized evaluation with economic accounting. Herein, a Chinese regionalized monetization model was applied to various indicators related to the environment, resource depletion and human health to assess ten typical rural food waste recycling technologies in Zhejiang province. The results reveal that biodrying and maturity and two bioconversion options are promising solutions, considering both environmental and economic impacts as well as the shifting of environmental impacts among different compartments as hidden risks. The monetization method proposed here could be applied to other sectors to support decision-making towards more sustainable develomet.
MainFood waste recycling is a key strategy to mitigate environmental impacts, enhance resource efficiency and protect human health1,2. In China, approximately 36% of the population lives in rural areas and generates approximately 280 Mt of solid waste per year, 25–60% of which is food waste (70–168 Mt)3,4. Currently, most rural food waste is landfilled or incinerated, accounting for 27–66 Mt of CO2-equivalent emissions (CO2e), or 4–10% of the greenhouse gas (GHG) emissions from the entire Chinese agriculture system5. The mismanagement of rural food waste also poses risks to air, water, soil and humans and results in resource depletion6. From a circular ecoomy perspective, rural food waste is rich in nutrients, offering the potential to be reused as a resource by technologies such as anaerobic digestion, composting, pyrolysis and bioconversion.The environmental and economic performance of these technological solutions should be evaluated on the basis of life-cycle assessment (LC) to capture potential impacts of inputs and emissions during waste conversion processes and to include credits as avoided environmental impacts from the application of products that replace the conventional ones7,8,9,10. A scoping review of 1,333 papers studying LCA and life-cycle costing (LCC) of food waste management technologies and analysing the most relevant publications (Supplementary Table 1) reveals that trade-offs exist among different environmental impacts, as well as among environmental and economic considerations during the life cycle of food waste. These trade-offs may hinder decision-making in technology promotion or in launching guidelines by govenments. A comprehensive evaluation is required to understand these trade-offs and support the selection of the most suitable rural food waste recycling technology11.Monetization converts environmental impacts caused by the release of pollutants or the use of natural resources to monetary values, so that the different types of ipacts (for example, water pollution, air pollution and soil contamination) with different units can be normalized and integrated in the same framework for comparison12. In addition, these monetary values fall into the concept of externality costs, which are unaccounted for in economic activities (for example, LCC including capital and operating costs) and may affect decision-making13. Their impacts on the economic feasibility of technologies (for example, break-even years) reveal the financial risks to business operators if these externalities are internalized as part of cash flows. Various life-cycle monetization methodologies have been developed in Europe and apan—for example, Ecovalue, Ecotax, ReCiPe, LIME and Environmental Prices11,14,15,16,17. However, given that monetization is affected by regional differences, a monetization method that is suitable for China and covers multiple environmental impacts is still lacking, although efforts have been made11,18,19.Here, following the Inernational Organization for Standardization (ISO) standard 14008-2019, we propose a Chinese regionalized method, ChinataxRCP, to monetize various LCA indicators related to the environment, resource depletion and human health20. We then demonstrate the use of ChinataxRCP by evaluating ten technologies for handling rural food waste using first-hand data from Zhejiang province (Supplementary Fig. 1)21. Similar to the Chinese average, Zhejiang generates 0.6 kg of rural waste per person per day, of which over 60% is food waste22. Zhejiang identified 1,970 pilot villages and established 1,078 rural food waste recycling stations by 2019, comprising 14,400 villages and11.5 million residents and covering the current representative rural food waste recycling options, which can potentially be applied in other provinces in China21,23. By applying our monetization method, we examined the trade-offs among the environmental impacts of the ten technologies, as well as those between the environment andthe economy. The results revealed the shifting of environmental impacts among air, water, soil and human health, as well as the risks induced by the different recycling technologies. Overall, the monetization approach developed in this study could support the development of effective policies and help entrepreneurs understand the financial risks associated with environmental pollutants during the implementation of these technologies from a socio-economic perspective.ResultsRegionalized life-cycle monetization of environmental impacts in ChinaIn this study, ChinataxRCP, a regionalized life-cycle monetization model, was built following ISO14008-2019 and is appliable to LCA ReCiPe 2016 indicators (Fig. 1)20. For environmental categories related to the emission of pollutants, the basis was environmental protection taxes representing societies’ willingness to pay. For those related to resource consumption (water and abiotic resources), resource taxes representing the restoration cost of pos natural source extraction were used to estimate monetization factors.Fig. 1: Flow chart for establishing the monetization framework in this work.The process is based on ISO 14001-2015 and ISO 14008-2019.Full size imageRegional monetization factors in China were compared with those from nine existing monetization methods that are applicable to other countries and are based on various theories to understand how different they are (Table 1, Fig. 2 and Supplementary Fig. 2). Our results are relatively close to monetization factors from models with a global scope but lower than those from models developed in European Union (EU) or Organisation for Economic Co-opeation and Development countries, due to their different levels of economic development. Moreover, monetization factors estimated on the basis of damage costs are generally higher than those based on abatement costs theory. One example is the indicator fine particulate matter formation (PMFP). Monetization factors for PMFP in EPS, E, Stepwise, MMG, Ecovalue, ReCiPe, EVR, LIME3 and ChinataxRCP were compared after the normalization15,16,24,25,26,27,28,29. The first seven are applicable to EU countries, except Stepwise, which has a global scope and includes the reduced working capacity caused by the damage to human health. Though based on a similar underlying theory, the monetization factor for PMFP in ChinataxRCP is lower than that in the EVR method due to the differences in economic levels in the EU and China. In addition, not all monetization factors based on damage cost theory from other countries are higher than those estimated by ChinataxRCP—for example, terrestrial acidification poential (TAP), freshwater ecotoxicity potential (FETP), marine ecotoxicity potential (METP) and terrestrial ecotoxicity potential (TETP). This indicates that substantial financial efforts have been leveraged to reduce the release of SOx to air and pollutants to water through environmental protection taxes in China, resulting in improed quality of air and water in the past few years (Table 1 and Fig. 2a).Table 1 Comparison of nine monetization methods in different countries with ChinataxRCPFull size tableFig. 2: Comparison of monetization factors.a, Monetization factors from ten monetization methods. b, Monetization factors from ChinataxRCP for 31 provinces in China. Ozone formation in b includes both ecosystem ozone formation potential (EOFP) and HOFP due to a statistical limitation. The sample size n used to derive statistics for a is 1 < n < 31. For each box, the thick horizontal lines represent median values; the boxes extended from the 25th to the 75th percentile of each grou’s distribution of values, or interquartile range; the vertical lines denote adjacent values (that is, the most extreme values within 1.5 times the interquartile range below and above the 25th percentile of each group); and the dots outside the range of adjacent values that are higher than 1.5 times and lower than 3 times the interqurtile range beyond either end of the box are outliers. DCB, dichlorobenzene.Source dataFull size imageProvincial monetization factors in ChinataxRCP were developed and compared. The results shown in Fig. 2b and Supplementary Fig. 3 demonstrate that most values for China’s eastern and southeastern areas are higher than those in other regions due to the differences in socio-economic status reflected by per capita income, except land use (LU), water consumption potential (WCP) and ionizing radiation (IR). These monetization factors are also affected by the local availability of agricultural land, water scarcity and local radiation intensity.Environmental impats of rural food waste recycling technologiesThe environmental performance of rural food waste recycling technologies (named T1 to T10—see Methods for the complete list of technologies) were assessed using the ReCiPe 2016 LCA characterization method with the ‘cradle-to-grave’ system boundaries (Supplementary Fig. 1). The functional unt (FU) is treating 1 t of rural food waste. The LCA results and the main contributors to each indicator are illustrated in Fig. 3 and Supplementary Fig. 4. The positive values are environmental burdens, while the negatives are credits from product substitution. In general, T1 and T10 performed the worst, mainly due to the negligible amount of products and therefore fewer credits being claimed from replacing the traditional ones. For global warming potential (GWP), T2–T9 resulted in a range of −61.7 to 90 kg CO2e per FU, leading to 80% to 115% of potential CO2 reductions compared with those from landfilling, partly due to biogenic carbon sequestered in comost or organic fertilizer after use in T2–T9 (Fig. 3a). Within these technologies, T6–T9 reduced GHGs the most due to the substantial amount of credits claimed from multiple products. These bioconversion technologies are recommended as the most suitable from the perspective of GHG mitigation, which falls within the scope of SustainableDevelopment Goal 13 (‘Climate action’). For air quality indicators (such as PMFP and human health ozone formation potential (HOFP) in Fig. 3e,f and other indicators in Supplementary Fig. 5), direct emissions from processes are the main contributors to the environmental burdens. However, except for T7, T2–T9 presented lower net environmental impact values for TAP and PMFP due to the substitution of credits. For water quality indicators (that is, WCP, freshwater eutrophication potential (FEP) and marine eutrophication potential (MEP)) shown in Fig. 3g–i and Supplementary Fig. 5, T6–T9 have the lowest impact values because the yielded products have the potetial to substitute soybean protein, which is a water-intensive product. For soil quality indicators (TAP, TETP and LU in Fig. 3d and Supplementary Fig. 5) and most human health indicators (human carcinogenic toxicity (HTc), IR, FETP, METP and human non-carcinogenic toxicity (HTnc) in Fig. 3h,i and Supplementary Fig. 5), T6–T9 present god environmental performance compared with the others. For indicators of resource depletion, mineral resource scarcity (SOP) was lower for all alternative technologies than for landfilling due to the resource recovery (Fig. 3b). Fossil fuel scarcity (FFP) was the lowest for T6–T9 because of the combined effects of lower energy consumption during the process and substantial environmental credits from crude oil/protein products (Supplementary Fig. 5). According to Sustainable Development Goal 12 (‘Responsible consumption and production’), technologies that minimize energy and resource consumption while maximizing resource reuse are recommended—that is, T6,T7 and T9. Overall, bioconversion technologies (T6–T9) achieved environmental savings in 11–14 of 18 impact indicators, followed by T2–T5 (5–8 of 18 indicators) (Supplementary Table 2).Fig. 3: LCA results of ten rural food waste treatment technologies.a–i, BAU, business as usual (landfilling); T1, mechanical drying; T2, biodrying and matrity; T3, solar-assisted composting; T4, underground anaerobic digestion; T5, heat pyrolysis carbonization; T6, bioconversion for black soldier fly (BSF); T7, bioconversion for BSF and bio oil; T8, bioconversion for red head fly (RHF) and bio oil; T9, heat hydrolysis and bioconversion; T10, enzyme production.Source dataFull size imageRisks across air, water, soil and human health associated with technological transitionEnvironmental impact indicators were grouped according to their relevance to human health and selected environmental compartments, such as air, water and soil, and the monetization results of each group were quantified (Fig. 4). The tota externality cost of each recycling technology is lower than that of the landfill option, where pollutants mainly affect air and soil quality (Fig. 4a). The overall negative values of T6–T9, as environmental credits, show that the substitution of soybean protein could reduce their environmental impacts, revealing promising benefits from rplacing soybean protein in animal feed30. The breakdown of results for air, water, soil and human health is illustrated in Fig. 4b. When recycling technologies are implemented instead of landfill disposal, the externality costs are reduced for air, soil (except for T1), water (except for T3 and T4) and resource depletion. However, externality costs related to human health for most recycling technologies are concerning except for T1 and T7. This is due to the direct process emissions (such as NOx and heavy metals) and fewer substitution credits from the products, leading to relatively higher environmental impacts on HOFP and human toxicity categories. ith regard to impacts on human health, those for T1 to T9 are 0.4 to 3 times higher than that of the landfill option. These results reveal that the transition from traditional to alternative recycling technologies may cause a shift of environmental impacts across compartments and may avoid risks in certain categories while compromising in thers (Fig. 4c). This was echoed by studies found in our scoping review10,31. For example, Ogunmoroti et al. concluded that diverting food waste from landfill disposal to anaerobic digestion can reduce GHG emissions by 74% but may pose challenges for Chinese provinces facing water scarcity due to water demand31.Fig. 4: Monetization results and environmental risk indexes.a,b, Monetization results for T1–T9 as the sum (a) and the breakdown for indicators grouped according to their relevance to air, water, soil, human health and resource depletion (b). c, Environmental risk indexes for rural food waste recycling technologies with values and their levelsindicated as no, low, moderate or high. T10 was not included due to its overall worse environmental and economic performance. Environmental risk indexes for each alternative technology are the ratios of their environmental externality cost to that of the landfill.Source dataFull size imageEconomic performance of rural food waste recycling tchnologiesThe results of our economic analysis of rural food waste treatment technologies using an LCC approach are presented in Supplementary Fig. 6 and Supplementary Data 1, sheet 21. The break-even year (in which the total cost and the total revenue are equal) was chosen as the indicator for analysing economic performance. The results reveal that T2, T6 and T9 could break even within ten years, while the others are not profitable. The breakdown of costs and revenues demonstrates that the capital costs of T1–T10 vary between €73.6 and €271.6 per FU and that their economic benefits mainly rely on a waste disposal fee (Supplementary Fig. 6a–c), excet for T6, which has substantial product revenues. This is in line with other studies21,32. The operational costs of T1 to T9 vary from €12 to €101 per FU, and their capital costs are between €4 and €14 per FU. Land occupation fees were also considered in the operational costs for T6–T9 due to their occupation of space for hatching, pretreatmnt, protein production and frass composting. When looking at net present values (NPVs), we illustrated potential trade-offs between them after normalization as the ratio to those of landfilling (Fig. 5a and Supplementary Data 1, sheet 23). Potential trade-offs were found for T2–T5, and definite trade-offs were found for T7 and T8. This dilemma is echoed by studies in our previous scoping review, though most of them focus on urban food waste6,8,10,12,33. For example, anaerobic digestion presents environmental benefits but with higher costs than the traditional incineration option in EU countries33. These conflicts between environmental and economic erformance may hinder policy and decision-making on technology choices for sustainable transitions.Fig. 5: Economic analysis and potential trade-offs between LCA and LCC analysis results for rural food waste recycling technologies.a, Normalized LCC results of processing 1 t of rural food waste (NPV5, NPV10 and NPV20 represent NPVs in the 5th,10th and 20th years for ten rural food waste recycling options) and potential trade-offs between LCA and LCC analysis results. b, Break-even years of rural food waste recycling options before and after integrating environmental and economic analyses.Source dataFull size imageIntegration of environmental and economic analysesTo address the above-mentioned trade-offs, we considered the monetized values of environmental impacts as externality costs in addition to capital and operational costs in LCC to integrate environmental and economic analyses of ten rural food waste recycling technologies. The NPVs and break-even years are indicators and are lised before and after the integration (Fig. 5b). The economic feasibility of T2 did not change substantially, while that of T4 improved and that of T3 worsened. Although T7 and T8 performed worse in the LCC analysis, benefits in terms of environmental credits have the potential to offset the economic loss associated with them once the stringent olicy of pollutant control is in place. These results indicate the impacts of externalities caused by their ‘internalization’, such as compensating those who have environmental credits and taxing those who cause environmental burdens. Overall, T2, T6 and T9 are preferable to the government after considering both environmental and economic perspectives.Substitution and policy scenario analysisAssumptions regarding product substitution and future policy setting may affect the decision preference7,34,35. Scenarios S1–S4 were set to investigate the influences of protein, biogas, compost and biochar substitutions on the monetization results (Supplemenary Methods). For example, larva protein was assumed to replace soybean protein in the default case but can alternatively replace fishmeal, though this is not preferred due to the potential insufficient level of nutrients (Fig. 6, S1). Biochar could be used as a soil amender36 (the default) or as coal. The latter offers negative monetization reults as monetary values of environmental credits (−€36.10 per t rural food waste) (Fig. 6, S2), but it is less preferable due to a relatively lower heating value. Different substitutes for biogas and compost (Fig. 6, S3 and S4) had an insubstantial impact on the monetization results, except for S3-VII. S5 and S6 were designed to investigate the effects of government subsidies and the implementation of pollution controls. Policy scenarios with and without subsidies (that is, a food waste disposal fee as a subsidy to encourage waste recycling) (Fig. 6, S5) indicate that T1, T5, T7, T8 and T10 would not be profitable even with good subsidies—that i, €30.50 per t food waste. When an average food waste disposal fee (€14.00 per t food waste) was applied, NPV20 was positive for only T6 and T9. No technology could survive under zero subsidies, indicating that the rural food waste recycling system relies heavily on government support, similar to insights from other studies23,37. Taking T9 as anexample, scenarios with and without pollution control are demonstrated in Fig. 6, S6. The notable operational costs from the implementation of pollution controls added to the total annual cost of T9 are not offset by the induced environmental benefits. Given this, we recommend that the local government provide financial support (that is, at least €5.87 per t food waste for T9) to reinforce pollution control measures in rural food waste management practices even considering externality costs. However, when assessing the switch from conventional technology to alternatives with high emissions, the application of our method may reveal different insghts—for example, the implementation of pollutant control would benefit the environment via externality reduction. Overall, the application of our method could support policymaking in technology transitions from landfilling to waste recycling.Fig. 6: Scenario analysis of monetization results using different substitution methods and under various olicy scenarios. (Note: EPtotal are the total environmental impacts of each indicator, EPavoid are the environmental credits of products that replace the conventional products, EPnet are the values obtained by subtracting EPavoid from EPtotal)One FU represents 1 t of rural food waste. The leftmost bar is the default case for all scenarios. S1 shows the monetization results comparison of protein substitution with soybean meal and fishmeal (T9, heat hydrolysis and bioconversion). S2 shows the monetization results comparison of biochar substitution with compost (with or without biochar soil effect) or coal (T5, heat pyrolysis carbonization). S3 i compost substitution with chemical fertilizer for T2 (biodrying and maturity technology) in different NPK ratios ranging from 0.4:0:0 to 1:1:1; the details are shown in Supplementary Table 7. S4 shows the monetization results comparison of biogas substitution with corn straw and coal (T4, underground anaerobic digestion). S5 shows the environmentl benefits comparison with and without food waste treatment subsidies (T9, heat hydrolysis and bioconversion). S6 shows the costs and benefits comparison of T9 with and without pollution control. BAU is landfill disposal. For environmental monetary values, the positive scores mean benefits gained from negative environmental impacts.Source dataFull size imageDiscussionFood waste is a global issue that is strongly linked to climate change, resource consumption and human health; therefore, it must be tackled with all these perspectives considered38,39. We synthesized empirical evidence via a scoping literature review and found that trade-offs exst among different environmental impacts, as well as among environmental and economic considerations, during the life-cycle management of food waste. As shown here, monetization methodologies integrating the life-cycle environmental impacts across different indicators, and with economic accounting as externalities, could provide practical solutionsto deal with these trade-offs and avoid regrettable decisions. Since these methods are geographically dependent, ChinataxRCP was established in this study and applied to a Chinese province to evaluate rural food waste management approaches.To foster a sound ecological environment, eliminate waste and improve resource use efficiency in China, policies and action guidelines have been launched with food waste recycling as one of the focuses. Zhejiang acted as a pioneer in practising many of these approaches since 2013. Ten typical recycling technologies for handling rural food waste have been demonstrated and evaluated in our study. The environental analysis results reveal that most of the options, especially bioconversion technologies (T6–T9), result in a reduction of GHG emissions compared with the landfill option, but they may compromise other indicators. For example, ozone formation potential is enlarged for T9, a technology combining oil extraction, compost production and bioconversin. Economic analysis via LCC also reveals that bioconversion technologies T7 and T8 are not economically feasible due to the larger capital costs, showing trade-offs between economic and environmental performance. On the basis of the regionalized ChinataxRCP method, T2 (biodrying and maturity), T6 and T9 (bioconversion processes) are recommended after tackling trade-offs between environmental and economic performance. These technologies differ from those in most studies dealing with urban food waste (for example, oil extraction combined with anaerobic digestion), which is large in volume and contains a greater proportion of out-of-home wast31,32.Food waste is a complex issue. The content of food waste is influenced by regional economic levels and diets, affecting treatment outcomes such as product quality/yield and therefore credits in our environmental and economic analyses. Sensitivity analyses (Supplementary Tables 3 and 4) showed that T9, T2 and T6 are more robust and have the potetial for widespread application in other provinces, most likely those with similar levels of economic development, such as Jiangsu, Shanghai and Guangdong. For those provinces with less developed economic levels, we recommend using our method for a comprehensive analysis that combines environmental and economic perspectives, a topic of particular interest to us for future research. In particular, we have shown that externality analysis based on grouped environmental indicators (for example, air, water, soil, human health and resource depletion) can quantify and visualize the environmental risks associated with these compartments. This willprovide valuable insights into the choice of technologies according to the priorities of decision makers40,41. The whole assessment process in this study could provide knowledge for the development of practical guidelines for more sustainable management of rural and even urban food waste. The environmental benefits we have identified could provide quatitative evidence to accelerate the implementation of subsidies or other supportive policies in the future.The quantitative, comprehensive and regionalized externality monetization method developed in our study could be applied to other sectors in China beyond food waste management. This includes support for policy and decision-making with regard to the transition of technologies, decisions on subsidies, the evaluation of services (for example, ecosystem and natural services) as a supplementary perspective42, and the awareness of inequality due to regional heterogeneity of resources and the environment.It should be noted that our model, lke other methods of integrating environmental and economic perspectives, has limitations and is limited to its application in China. Monetary values are sensitive to environmental taxes and market prices of goods, which are highly dependent on regional circumstances. We recommend that the development of monetization factors for other countries should fllow the ISO standard (14008-2019) and that their application should be appropriate to the geographical scope of the studies.MethodsLiterature review of LCA and LCC studies on food waste managementA total of 1,333 publications were identified through a literature search in the Web of Science, Google Scholar, ScienceDirect and Scopus with a date range of 2017 to 2022 and using the topic words ‘food waste’ AND ‘life cycle assessment’ OR ‘life cycle’ OR ‘LCA’ OR ‘environmental impact’ (1,092); ‘food waste’ AND ‘life cycle assessment’ OR ‘life cycle’ OR ‘LCA’ OR ‘environmental impact’ AND ‘life cycle costing’ OR ‘economic analysis’ OR ‘life-ycle costing’ OR ‘cost benefit analysis’ OR cost (225); ‘food waste’ AND ‘life cycle assessment’ OR ‘life cycle’ OR ‘LCA’ OR ‘environmental impact’ AND ‘monetization’ OR ‘monetary value’ OR ‘monetary’ OR ‘externality costs’ OR ‘externality’ OR ‘externalities’ (16). We then removed duplicates, screened the abstracts and assessed the full texts to includethose papers that assessed environmental and/or economic indicators related to food waste reutilization and presented potential trade-offs (between environmental and economic performance). We identified 21 most relevant publications among the 1,333; these are summarized in Supplementary Table 1.Establishment of a regional monetization model for ChinaChinataxRCP is a regionalized monetization model applicable to ReCiPe 2016 LCA indicators based on regional environmental protection taxes, resource taxes and carbon abatement costs in China (Supplementary Data 1, sheets 1–16). Targeting environmental protection and placing more accountabiliy on local governments, the environmental protection taxes were launched in 201843 and are used in this paper as the basis for estimating monetization factors and emissions indicators (PMFP, FEP, MEP, EOFP, HOFP, TAP, FETP and METP). Regarding resource depletion indicators (FFP, SOP, WCP and LU), the intention of charging resource taxes is to support therestoration of the environment after mining, promoting more efficient mining and responsible consumption. The industrial abatement costs of CO2 and ozone-depleting substances were used for estimating the monetization factors of GWP and ozone layer depletion (ODP). Human-health-related indicators were estimated by using the value of life expectancy of Chinese residents (HTc and HTnc). Other indicators (that is, TETP and IR) were adopted from the globally applicable models and adjusted. Following ISO 14008-2019, per capita income in each province was used to yield regionalized monetization factors adjusted from the Chinese average. The cmparison of monetization factors in different provinces was visualized using FineBI version 5.1 (ref. 44).PMFP, FEP, MEP, EOFP, HOFP and TAPThe monetization factors for PMFP, FEP, MEP, EOFP, HOFP and TAP in 31 provinces were estimated according to equations (1)–(3), considering that each of these environmental indicators is caused by multiple environmenta pollutants45. The data for the total amount of pollutants in each province ai,j,m and pollution equivalent values bi,j,m were collected according to information from the National Bureau of Statistics and the National Bureau of Environmental Protection. In China, the environmental taxes are defined for each pollutant as RMB per pollutant equivalent (converted to euros in the final results after estimation). The pollutant equivalent value is defined as the mass of each pollutant per pollutant equivalent as a ratio reflecting the severeness of each emission polluting the environment. The influence potential coefficient ei,j,m representsthe ratio of impacts caused by emission i over impacts by total emissions relevant to indicator j after considering characterization. The impacts of PM2.5 are approximately four times as damaging as those of PM1014,46. The monetary intermediate factor of PMFP was calculated by converting PM10 to PM2.5 by multiplying by 4 due to the lack of a characterizatin factor between the taxed pollutant PM10 and the LCA indicator PM2.5 (Supplementary Data 1, sheet 3). The monetization factors were estimated as follows:$${c}_{i,,j,m}=frac{1}{{b}_{i,,j,m}} imes {r}_{i,,j,m}$$
(1)
$${e}_{i,,j,m}=frac{{{
m{f}}}_{i,,j,m} imes {a}_{i,,j,m}}{sum ({{
m{f}}}_{i,,j,m} imes {a}_{i,,j,m})}$$
(2)
$${w}_{j,m}=sum left({e}_{i,,j,m} imes {c}_{i,,j,m}
ight)$$
(3)
where ai,j,m is the total emissions amount in 2020 for pollutant i related to indicator j in province m; bi,j,m is the pollutant equivalent value of emission i related to indicator j in provnce m; fi,j,m is the characterization factor of pollutant i for LCA indicator j in province m; ri,j,m is the tax on emission i in 2018 for indicator j in province m, in yuan (converted to euros in the final results) per pollutant equivalent (because the charged taxes are the same in 2018 as in 2020, the discount rate is not used in this study for estimation); ci,j,m is the monetar intermediate factor for indicator j related to pollutant i in province m; ei,j,m is the influence potential coefficient of pollutant i for LCA indicator j in province m; and wj, m is the monetization factor of indicator j in province m.FETP and METPThe monetization factors for FETP and METP were calculated with equations (4) and (5) using monetary adjustment factors, midpoint characterization factors for LCA indicators, and species densities in fresh water and in marine water for each province47:$${w}_{{{mathrm{FETP}}},m}=frac{1}{{b}_{i,,j,m}} imes {r}_{i,,j,m}$$
(4)
$${w}_{{mathrm{METP}}m}={w}_{{mathrm{FETP}},m} imes frac{{mathrm{SD}}_{{mathrm{marine}}}}{{mathrm{SD}}_{{mathrm{fresh}}}} imes frac{{mathrm{CF}}_{{mathrm{marine}}}}{{mathrm{CF}}_{{mathrm{fresh}}}}$$
(5)
where SDmarine is the species density in marine water, in species per m3 (that is, 3.46 × 10−12; ref. 47); SDfresh is the species density in fresh water, in species per m3 (that is, 7.89 × 1010; ref. 47); CFmarine is the conversion factor from midpoint to endpoint for METP, for species in marine water, in yr/kg 1,4-DCB equivalent (that is, 1.05 × 10−10; ref. 47); CFfresh is the conversion factor from midpoint to endpoint for FETP, for species in fresh water, in yr/kg 1,4-DCB equivalent (that is, 6.95 × 10−10; ref. 47); wFETP,m is the monetization factor for FETP in province m in China; and wMETP,m is the monetization factor for METP in province m in China.FFP and SOPThe monetization factors for FFP and SOP in 31 provinces were estimated according to resource taxes on copper and cude oil in each province and their trading price in China. An average oil price over the past ten years was chosen to avoid fluctuations due to COVID-19. The calculations were done using equations (6) and (7):$${w}_{{{mathrm{SOP}}},m}={mathrm{Average}}({{mathrm{Cu}}}_{{{mathrm{price}}},2020}) imes {{mathrm{Cu}}}_{{{mathrm{tax}}},m}$$
(6)
$${w}_{{{mathrm{FFP}}},m}={mathrm{Average}}let({{mathrm{Oil}}}_{{{mathrm{price}}},2011-2020}
ight) imes {{mathrm{Oil}}}_{{{mathrm{tax}}},m}$$
(7)
where Cutax,m is the copper tax on mineral processing in province m and Oiltax,m is the oil tax in province m.WCP and LUThe average resource taxes (or fees) on surface water in each province were used as the monetization factors of the indicator WCP in 31 provinces. However, land use tax (either on urban land or on cultivated land) is not appropriate due to its intention to improve land use efficiency and circulation, rather than biodiversity damage during land useand transformation as captured in the ReCiPe 2016 handbook47. The primary monetization value of LU was therefore adopted from the monetization model of ReCiPe and adjusted following the value transfer approach in ISO 14008-2019. The estimation details are shown in equations (8) to (16):$${{mathrm{MSA}}}_{{mathrm{f}} o {mathrm{c}}}=frac{left({{S}_{{mathrm{f}}}-S}_{{mathrm{c}}}
ight)}{{S}_{{mathrm{f}}}}$$
(8)
$${{mathrm{MSA}}}_{{mathrm{g}} o {mathrm{c}}}=frac{left({{S}_{{mathrm{g}}}-S}_{{mathrm{c}}}
ight)}{{S}_{{mathrm{g}}}}$$
(9)
$${{mathrm{MFR}}}_{{mathrm{CPI}}}=frac{{{mathrm{CPI}}}_{{{mathrm{EU}}},2021}}{{{mathrm{CPI}}}_{{{mathrm{EU}}},2020}}$$
(10)
$${{w}^{{prime} }}_{{{mathrm{LU}}},{mathrm{f}},2020}={P}_{{mathrm{f}}} imes {{mathrm{MSA}}}_{{mathrm{f}} o {mathrm{c}}} imes frac{{{w}^{{prime} }}_{{{mathrm{LU}}},{mathrm{f}},2021}}{10{,}000} imes {mathrm{MFR}}_{{mathrm{CPI}}}$$
(11)
$${{w}^{prime} }}_{{{mathrm{LU}}},{mathrm{g}},2020}={P}_{{mathrm{g}}} imes {{mathrm{MSA}}}_{{mathrm{g}} o {mathrm{c}}} imes frac{{{w}^{{prime} }}_{{{mathrm{LU}}},{mathrm{g}},2021}}{10{,}000} imes {{mathrm{MFR}}}_{{mathrm{CPI}}}$$
(12)
$${{w}^{{prime} }}_{{{mathrm{LT}}},{mathrm{f}},2020}={0.5 imes P}_{{mathrm{f}}} imes {{mathrm{MSA}}}_{{mathrm{f}} o {mathrm{c}}} imes frac{{{w}^{{prime} }}_{{{mathrm{LT}}},{mathrm{f}},221}}{10{,}000} imes frac{1}{{t}_{{{mathrm{rel}}},{mathrm{f}}}} imes {{mathrm{MFR}}}_{{mathrm{CPI}}}$$
(13)
$${{w}^{{prime} }}_{{{mathrm{LT}}},{mathrm{g}},2020}=0.5 imes {P}_{{mathrm{g}}} imes {{mathrm{MSA}}}_{{mathrm{g}} o {mathrm{c}}} imes frac{{{w}^{{prime} }}_{{{mathrm{LT}}},{mathrm{g}},2021}}{10{,}000} imes frac{1}{{t}_{{{mathrm{rel}}},{mathrm{g}}}} imes {{mathrm{MFR}}}_{{mathrm{CPI}}}$$
(14)
$${{mathrm{MFR}}}_{{mathrm{PPP}}}=frac{{{mathrm{PPP}}}_{{{mathrm{China}}},2020}{{{mathrm{PPP}}}_{{{mathrm{EU}}},2020}}$$
(15)
$${w}_{{{mathrm{LU}}},2020,m}=left(sum {{w}^{{prime} }}_{{{mathrm{LU}}},{mathrm{f}},2020}+sum {{w}^{{prime} }}_{{{mathrm{LT}}},2020,m}
ight) imes {{mathrm{MFR}}}_{{mathrm{PPP}}} imes {{mathrm{MFR}}}_{{{mathrm{lut}}},m}$$
(16)
where Pf is the proportion of forest biomes to the total global terrestrial area (that is, 40%; ref. 48); Pg is the proportion of grassland biomes to the total global terretrial area (that is, 60%; ref. 48); w′LU,2021 is the monetization factor of LU by land type (forest or grassland) according to https://trueprice.org/ (Supplementary Data 1, sheet 13); w′LT,2021 is the monetization factor of land transformation according to https://trueprice.org/; w′LU,2020 is the average monetization factor of LU in China; wLU,2020,m is the monetization factor of LU in province m; MSAf→c is the mean species abundance when forest is transformed to cropland; MSAg→c is the mean species abndance when grassland is transformed to cropland; Sf is the relative species richness of forest (that is, 1; refs. 48,49); Sg is the relative species richness of grassland (that is, 0.67; refs. 48,49); Sc is the relative species richness of annual cropland (that is, 0.4; refs. 48,49); MFRlut,m is the modification factor of land use tax in province m; trel, f is the recovery time (years) for species richness of forest biomes, years; trel, g is the recovery time (years) for species richnessof grassland biomes, years; CPI is consumer price index; and PPP is purchasing power parity.GWPThe average monetization factor for GWP (approximately €0.052 per kg CO2e) was estimated using the industrial abatement costs of CO2 in five major carbon emission sectors in China—that is, construction, cement, power, the iron and steel industry, and the petrochemical industry (the data are derived from the 2020 report by the Ministry of Ecology and Environment in China, and the references are shown in Supplmentary Data 1, sheet 10):$${w}_{{mathrm{GWP}},{mathrm{average}}}=frac{mathop{sum }
olimits_{t=1}^{t=6}left({{
m{AC}}}_{t} imes {{
m{AP}}}_{t}
ight)}{mathop{sum }
olimits_{t=1}^{t=6}{{
m{AP}}}_{t}}$$
(17)
where ACt is the amount of CO2 abated in sector t (including five major sectors—construction, cement, power, the iron and steel industry, and the petrochemical industry); APt is the CO2 abatement cost in sector t, including the five sectors mentioned above; and wGWP,aerage is the average monetization factor for GWP in China.ODPThe monetization factor for ODP was estimated according to the total investment under Ozone Depleting Substances Abatement regulations in China (US$53.64 million in 2006) and the total amount of ozone-depleting substances reduced (25,000 t)45,50. The value was then adjusted to regional values using the adjustment factors of ozone concentration and provincial income per capita according to equations ( where Ej, m is the economic modifying factor for indicator j in province m in China, Exm is the per capita income in province m in China, Investtotal is the total investment in ozone-depleting substances abatement (in US$), ODStotal is the total reduction amount of ozone-depleting substances (in tonnes), MFozone, m is the modification factor of ODP in province m, OCozone, m is the average ozone concentration (theaverage maximum daily ozone concentration at the 90th percentile for eight hours) in province m in 2020 (in μg m−3) an wODP,n, m is the monetization factor of ODP in year n in province m.HTc and HTnc
HTc and HTnc were estimated using the value of life expectancy of Chinese residents in 2016 (€55,400 per disability-adjusted life year (DALY))18, converted to kg 1,4-DCB equivalent using factors in the USEtox model (https://usetox.org/model) and customized to values for 31 provinces in China using per capita income. The calculations were done using equations (22) and (23):$${V}_{2020}=frac{{{
m{CPI}}}_{2020}}{{{
m{CPI}}}_{2018}} imes {V}_{2018}$$
(22)
$${w}_{{{mathrm{HT}}}_{j,m}}=sum {V}_{2020} imes {{mathrm{CF}}}_{({{mathrm{non}}}){{mathrm{cancer}}}} imes {E}_{j,m}$$
(23)
where Vn is the value of life expectancy of Chinese residents in the year n (that is, willingness-to-pay value per DALY); CF(non)cancer is the conversion factor from DALY t kg 1,4-DCB equivalent (confcancer equals 3.32 × 10−6 and confnon-cancer equals 2.28 × 10−7, according to the Usetox model); CPIn is the cosumer price index in China in year n; and ({w}_{{{{mathrm{HT}}}}_{j,m}}) is the monetization factor of human toxicity (cancer on-cancer) in province m.TETPTETP was estimated starting from adopting the value from the Stepwise model for developed countries in 2008 (approximately 1–2% of the gross domestic product), which is similar to the current environmental protection expenditures in China in 2020 according to statistics from the Ministry of Ecology and Environment of the People’s Republic of China (https://www.mee.gov.cn/hjzl/sthjzk/sthjtjnb/). Per capita income and fertilizing intensity of phosphate in each province were used to adjust the normalization results of TETP in each province. The formulas are shown as equations (24) to (27):$${{mathrm{Intense}}}_{m}=frac{{m}_{{mathrm{ph}}}}{{S}_{m}} imes frac{{M}_{{mathrm{ph}}} imes 2}{{M}_{{mathrm{P}}}_{2}{{mathrm{O}}}_{5}}}$$
(24)
$${{mathrm{MFR}}}_{{{mathrm{intensity}}},m}=frac{{{mathrm{Intense}}}_{m}}{{{mathrm{aerage}}}({{mathrm{Intense}}}_{m})}$$
(25)
$${{w}^{{prime} }}_{{mathrm{TETP}}}={{w}^{{prime} }}_{{{mathrm{TETP}}},{{mathrm{Stepwise}}}} imes frac{{{
m{CPI}}}_{{{mathrm{EU}}},2019}}{{{
m{CPI}}}_{{{mathrm{EU}}},2020}} imes frac{{{
m{PPP}}}_{{{mathrm{China}}},2020}}{{{
m{PPP}}}_{{{mathrm{EU}}},2020}}$$
(26)
$${w}_{{{mathrm{TETP}}},m}=sum {{w}^{{prime} }}_{{{mathrm{TETP}}},m} imes {{mathrm{MFR}}}_{{{mathrm{intensity}}},m} imes {E}_{j,m}$$
(27)
where mph is the consumption amount of phosphate in province m, Sm is the total sown area of crops in province m, MFRintensity, m is the modification factor of fertilization intensity in province m, Intensem is the application intensity of phosphorus in province m, Mph is the molecular weight of phosphorus (that is, 31), ({M}_{{{mathrm{}}}_{2}{{mathrm{O}}}_{5}}) is the molecular weight of phosphorus pentoxide (that is, 142), w′TETP,Stepwise is the monetization factor of TETP for the Stepwise model in 201, w′TETP is the average monetization factor for TETP in China, wTETP, m is the monetization factor for TETP in province m in China, CPIc,n is the consumer price index in country c in year n and PPPc,n is the purchasing power parity in country c in year n.IRConsidering that the monetization factors in Stepwise and EPS are globally applicable, the average of their IR monetization values in 2020 was used and adjusted for the Chinese average and then customized for 31 provinces using per capita income, air absorbed dose rate, and radiation concentration in aerosol and deposition51. The calculations were done as follows:$${{mathrm{MFR}}}_{m}=frac{{{mathrm{DR}}}_{m}}{{{mathrm{Average}}}({{mathrm{DR}}}_{m})}$$
(28)
$${w}_{{{mathrm{IR}}},m}={{mathrm{IR}}}_{{mathrm{Ecotax}}} imes {frac{{{{mathrm{CPI}}^{{prime} }}_{{{mathrm{EU}}},2020}}{{{{mathrm{CPI}}}^{{prime} }}_{{{mathrm{EU}}},2019}} imes frac{{{
m{PPP}}}_{{{mathrm{China}}},2020}}{{{
m{PPP}}}_{{mathrm{EU}}},2020}}times {{mathrm{MFR}}}_{m} imes {E}_{m}$$
(29)
where IREcotax is the average monetization factor of IR in Ecotax 2006 in Switzerland (in € per kBq 60Co to air), CPI′c,n is the consumer price index in Europe in year n, DRm is the air absorption dose rate of γ-rays in province m (in nGy h−1) and MFRm is the modification factor of radiation in province m.Technology descriptions, goals, scope and assumptionsTen typical technologies applied in Zhejiang province were investigated on site for a comparative study, as Zhejiang has explored more options for rural food waste governance than other provinces21,52,53. Rural food waste in this study represents food waste generated and collected in Chinese rural areas. The percentage of food waste in out-of-home settings is below 1% according to expert opinon, and it is therefore negligible.
BAU is the most conventional pathway in rural China. Rural food waste is collected, 40% of the wastewater is removed for wastewater treatment and th remaining dry materials are sent to a centralized landfill site.
T1 (mechanical dehydration technology) includes impurity sieving and treatment, mechanical dehydration and wastewater treatment, incineration of solid organics, and flue gas purification. Instead of being used as fertilizer, the solid organics are incinerated for energy because they did not meet the Chinese standard NY/T 525–2021 after multiple tests.
T2 (biodrying and maturity process) follows a similar procedure as T1, except that the solid organics can be used as compost due to a strict strengthening maturity process after the biodrying step.
T3 (solar-assisted composting) is another composting option that differs from T2 in the solid organic processing ste. Rural food waste is piled up in the ‘sunshine room’ for composting with the aid of forced ventilation and mechanical agitation.
T4 (underground anaerobic digestion) is a small-scale anaerobic digestion technology (<5 t d−). After impurity sieving, rural food waste is stored underground for anaerobic digesting. The solid content in the feedstock is kept lower than 15%. The produced biogas can be used after desulfurization. The mixture of solid–liquid digestate is used directly as fertilizer.
T5 (heat pyrolysis carbonization technology) is used to produce biochar. The raw feedstock is sieved, dehydrated and dried before heat pyrolysis to produce approximately 10% biochar. After processing, pathogens and antibiotics in food waste can be eliminated with low dioxin emissions. The produced biochar can be used as compost and is effective for carbon sequestration.
T6 (bioconversion for BSF) is a typical bioconversion technlogy used to produce BSF and compost. Rural food waste with impurities is manually sorted, and then rice hull powder is mixed with the food waste to breed larvae rich in multiple amino acids and frass, followed by frass composting. The larvae can be dried for storage and sles with high added value.
T7 (bioconversion for BSF and bio oil) is similar to T6, except that another by-product, bio oil, is produced after a three-phase separation. The amounts of BSF and frass are less than those produced by T6.
T8 (bioconversion for RHF and bio oil) is another bioconversion technology to produce RHF, bio oil and frass with a similar production route to T7, though it breeds different larva types. The compost amount for T8 is greater than that for T7, whereas the amounts of protein and bio oil are smaller for T8 than for T7.
T9 (dry-heat hydrolysis and bioconversion) is coupled with livestock and poultry manure disposal to produce SF, bio oil and compost. In this process, dry-heat hydrolysis at 90–100 °C for two hours is applied to improve the bio oil separation efficiency.
T10 (enzyme production) aims to produce enzymes with rural food waste by adding 1/3 part sugar and three parts tap water to one part food waste. However, he enzymes are difficult to separate and may be ultimately treated as wastewater54.
The FU for the LCA and LCC studies is processing 1 t of rural food waste. The system boundary begins with the transport of the collected rural food waste and ends with the disposal and use of the final products. The system includes the production of other raw materials used in all unit processes, transport, processing, product use and waste discard. The final products obtained can replace traditional and similar goods on the market (Supplementary Fig. 1). The assumptions are shown in Supplementary Data 1, sheet 0. Material and carbon flow analyses (Supplementary Fig. 4) wer applied.Environmental and economic analysis by LCA and LCCThe life-cycle inventories of the technologies are presented in Supplementary Table 5. LCA was performed using GaBi v10 software (http://www.gabi-software.com/). Eighteen LCA categories in ReCiPe 2016 were assessed. To analyse the economic feasibility of rural food waste anagement technologies, we performed LCC with results indicated by NPVs and break-even years. The NPV5, NPV10 and LCA results of treatment per tonne of rural food waste were normalized to reveal potential trade-offs between the LCA and LCC results. Detailed information on the indicators is presented in the Supplementary Information. Environmental burdens in this study are from raw materials, energy and waste discharge, while environmental credits mainly result from the substitution of conventional products. The net environmental impact results are equal to the differences between environmental burdens and credits (Supplementary Data 1, sheets 17–21).Risk analyis across air, water, soil and human healthThe built regionalized monetization model was applied to estimate the externality costs of each LCA indicator. The indicators were then grouped into five categories according to their relevance to water (including WCP, FETP, FEP, METP and MEP), air (including GWP, PMFP, EOFP and ODP), soi (including cultivated LU, TAP and TETP), human health (including HTc, HTnc, IR and HOFP) and resource depletion (including SOP and FFP)7,8 (Supplementary Table 6 and Supplementary Data 1, sheet 22). Ratios of the externality sum of each recycling technology in the above-mentioned groups to that of landfilling were calculated using equation (30). These ratios, Riskt,k, are considered as environmental risk indexes, revealing the magnitude of environmental risk induced by the technology transition. When the values of Riskt,k are in the range of <−3, −3 to 0, 0 to 1 and >1, they are defined as ‘No risk’, ‘Low risk’, ‘Moderate risk’ and ‘High risk’, respectvely:$${{mathrm{Risk}}}_{t,k}=frac{{{{mathrm{EC}}}^{{prime} }}_{t,k}}{{{mathrm{EC}}}_{t,k}}$$
(30)
where Riskt,k is the environmental risk related to compartment k (k represents air, water, soil, human health or resource depletion) for reuse technology t, EC′t,k is the externality costs related to category k for reuse tehnology t and ECt,k is the externality costs related to category k for the landfilling of rural food waste.Environmental and economic analysesIn addition to traditional incomes and costs in LCC, monetized values of the LCA results were included as externality costs. NPVs and break-even years remained indicators to present the integrated results (Supplementary Data 1, sheet 20).Sensitivity and scenario analysesSensitivity analysis was conducted with a one-at-a-time approach by adjusting 10% of each environmental factor (that is, inventory feedstock) and economic factor (that is, product revenue, capital investment and operational cost)7, which are preented in the Supplementary Information. The choices of product substitution for protein, biochar, compost and biogas in ten food waste recycling technologies (S1–S4) were analysed to investigate their influences. Detailed information is described in Supplementary Tables 7 and 8 and in Supplementary Data 1, sheet 25. The food waste recyclingtechnologies selected for scenario studies and the baseline are indicated in brackets:S1: protein substitution for fishmeal (T9, soybean meal substitution at baseline).S2: biochar substitution for compost or coal (T5, without biochar soil effect in the baseline).S3: compost substitution with different NPK ratios (T2, NPK ratio is 0.4:0:0 at baseline).S4: biogas substitution for coal (T4, biogas from manure substitution at baseline).S5: comparison with different food waste treatment subsidies (T1–T10, €30.5 per t food waste at baseline).S6: comparison under different environmental governance conditions (T9, pollution control is in place in the baselie).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
The data supporting the findings of this study can be found in Supplementary Data 1. Source data are provided with this paper.
ReferencesFesenfeld, L., Rudolph, L. & Bernauer, T. Policy framing, design and feedback can increase public support for costly food waste regulation. Nat. Food 3, 227–235 (2022).Article
PubMed
Google Scholar
Makov, T., Shepon, A., Krones, J., Gupta, C. & Chertow, M. Social and environmental analysis of food waste abatement via the peer-to-peer sharing economy. Nat. Commun. 11, 1156 (2020).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Zhang, Y. Analysis on the present situation of rural domestic waste treatment in China. Adv. Environ. Prot. 07, 373–379 (2017).Article
CAS
Google Scholar
The Seventh National Population Census (National Bureau of Statistics of China, 2020); http://www.stats.gov.cn/english/PressRelease/202105/t20210510_1817188.htmlXue, L. et al. China’s food loss and waste embodies increasing environmental impacts. Nat. Food 2, 519–528 (2021).Article
PubMed
Google Scholar
Albizzati, P. F., Tonini, D. & Astrup, T. F. A quantitative ustainability assessment of food waste management in the European Union. Environ. Sci. Technol. 55, 16099–16109 (2021).Article
ADS
CAS
PubMed
Google Scholar
Liu, F., Liu, H., Yang, N. & Wang, L. Comparative study of municipal solid waste incinerator fly ash reutilization in China: environmental and economic performances. Resour. Conserv. Recycl. 169, 105541 (2021).Article
Google Scholar
Edwards, J., Burn, S., Crossin, E. & Othman, M. Life cycle costing of municipal food waste management systems: the effect of environmental externalities and trasfer costs using local government case studies. Resour. Conserv. Recycl. 138, 118–129 (2018).Article
Google Scholar
Martinez-Sanchez, V. et al. Evaluation of externality costs in life-cycle optimization of municipal solid waste management systems. Environ. Sci. Technol. 51, 3119–3127 (2017).Article
ADS
CAS
PubMed
Google Scholar
Nordahl, S. L. et al. Life-cycle greenhouse gas emssions and human health trade-offs of organic waste management strategies. Environ. Sci. Technol. 54, 9200–9209 (2020).Article
ADS
CAS
PubMed
Google Scholar
de Adelhart Toorop, R., Yates, J., Watkins, M., Bernard, J. & de Groot Ruiz, A. Methodologies for true cost accounting in the food sector. Nat. Food 2, 655–663 (2021).Article
PubMed
Google Scholar
Shapiro-Bengtsen, S., Hamelin, L., Bregnbæk, L., Zou, L. & Münster, M. Should residual biomass be used for fuels, power and heat, or materials? Assessing costs nd environmental impacts for China in 2035. Energy Environ. Sci. 15, 1950–1966 (2022).Article
Google Scholar
Baker, L., Castilleja, G., De Groot Ruiz, A. & Jones, A. Prospects for the true cost accounting of food systems. Nat. Food 1, 765–767 (2020).Article
PubMed
Google Scholar
Arendt, R., Bachmann, T. M., Motoshita, M., Bach, V. & Finkbeiner, M. Comparison of different monetization methods in LCA: a review Sustainability 12, 10493 (2020).Article
Google Scholar
De Bruyn, S. B. et al. Environmental Prices Handbook (CE Delft, 2018).Inaba, A. & Itsubo, N. Preface of LIME3. Int. J. Life Cycle Assess. 23, 2271–2275 (2018).Article
Google Scholar
Eldh, P. & Johansson, J. Weighting in LCA based on ecotaxes—development of a mid-point method and experiences from case studies. Int. J. Life Cycle Assess. 11, 81–88 (2006).Article
Google Scholar
Wang, Q. et al. Life cycle assessment an the willingness to pay of waste polyester recycling. J. Clean. Prod. 234, 275–284 (2019).Article
CAS
Google Scholar
Amadei, A. M., De Laurentiis, V. & Sala, S. A review of monetary valuation in life cycle assessment: state of the art and future needs. J. Clean. Prod. 329, 129668 (2021).Article
Google Scholar
Monetary Valuation of Environmental Impacts and Related Environmental Aspects (International Organization for Standardization, 2019); https://wwwiso.org/standard/43243.htmlWu, W., Li, X. & Zheng, L. Resource Reuse Technologies of Rural Food Waste in Villages and Towns (in Chinese). ISBN: 9787030690609 (China Science Publishing & Media Ltd., 2021).Xiangru, L. Primary Study on Bio-drying and Maturing of Rural Biodegradable Waste (Zhejiang Univ., 2019).Zou, Y. Out of sorts: rural waste problems in China and the United States. New Security Beat https://www.newsecuritybeat.org/2020/10/sorts-rural-waste-problems-china-united-states/ 2020).Steen, B. Calculation of monetary values of environmental impacts from emissions and resource use: the case of using the EPS 2015d Impact Assessment Method. J. Sustain. Dev. 9, 15 (2016).Article
Google Scholar
Weidema, B. P. Using the budget constraint to monetarise impact assessment results. Ecol. Econ. 68, 1591–1598 (2009).Article
Google Scholar
De Nocker, L. D. & Debacker, W. Annex Monetisation of the MMG Method (Update 2017) (Public Waste Agency of Flanders (OVA), 2018).Ahlroth, S. F. & Finnveden, G. Ecovalue08—a new valuation set for environmental systems analysis tools. J. Clean. Prod. 19, 1994–2003 (2011).Article
Google Scholar
Monetisation Factors for True Pricing v.2.0.3 (T.P. Foundation, 2021); https://trueprice.org/EVR Model (Sustainability Impact Metrics, 2022); https://www.ecocostsvalue.com/evr/Naylor, R. L. et al. A 20-year retrospective review of global aquaculture. Nature 591, 551–563 (2021).Article
ADS
CAS
PubMed
Google Scholar
Ogunmoroti, A., Liu, M., Li, M. & Liu, W. Unraveling the environmental impact of current and future food waste and its management in Chinese provinces. Resour. Environ. Sustain. 9, 100064 (2022).
Google Scholar
Chen, T. et al. Economics analysis of food waste treatment in China and its influencing factors. Front. Environ. Sci. Eng. 15, 33 (2020).Article
Google Scholar
Tonini, D. et al. Quantitative sustainability assessment of household food waste mangement in the Amsterdam Metropolitan Area. Resour. Conserv. Recycl. 160, 104854 (2020).Article
PubMed
PubMed Central
Google Scholar
Liu, B. & Rajagopal, D. Life-cycle energy and climate benefits of energy recovery from wastes and biomass residues in the United States. Nat. Energy 4, 700–708 (2019).Article
ADS
CAS
Google Scholar
Hanserud, O. S., Cherubini, F., Ogaard, A. F., Muller, D. B. &am; Brattebo, H. Choice of mineral fertilizer substitution principle strongly influences LCA environmental benefits of nutrient cycling in the agri-food system. Sci. Total Environ. 615, 219–227 (2018).Article
ADS
CAS
PubMed
Google Scholar
Yang, Q. et al. Prospective contributions of biomass pyrolysis to China’s 2050 carbon reduction and renewable energy goals. Nat. Commun. 12, 1698 (2021).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Wang, A. et al. Rural solid waste management in China: status, problems and challeges. Sustainability 9, 506 (2017).Article
Google Scholar
IEA: Latest News, Commentaries and Reports (IEA, 2020); https://www.iea.org/Herrero, M. et al. Innovation can accelerate the transition towards a sustainable food system. Nat. Food 1, 266–272 (2020).Article
Google Scholar
Gil, J. Dietary carbon–water–food nexus. Nat. Food 3, 187 (2022).Article
Google Schola
D’Odorico, P. et al. The global food–energy–water nexus. Rev. Geophys. 56, 456–531 (2018).Article
ADS
Google Scholar
Costanza, R. et al. The total value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).Article
ADS
CAS
Google Scholar
Environmental Protection Tax (State Taxation Administration, 2018); http://www.chinatax.gov.cn/chinatax 610/c5178958/content.htmlFineBI v.5.1 https://www.finebi.com/ (FanRuan Software, 2019).Niu, J. Monetary Methods of Life Cycle Impact Assessment for Inert Construction and Demolition Waste (in Chinese)(Qinghai Univ., 2019); https://doi.org/10.27740/d.cnki.gqhdx.2019.000102Bare, J. TRACI 2.0: the tool for the reduction and assessment of chemical and other environmental impacts 2.0. Clean Technol. Environ. Policy 13, 687–696 (2011).Article
CAS
Google Scholar
Huijbregts, M. et al. ReCiPe 2016 (RIVM, 2016); https://rivm.openrepository.com/handle/10029620793RIVM (National Institute for Public Health and the Environment, 2017); https://www.rivm.nl/ende Baan, L., Alkemade, R. & Koellner, T. Land use impacts on biodiversity in LCA: a global approach. Int. J. Life Cycle Assess. 18, 1216–1230 (2013).Article
Google Scholar
China Ecological Environment Bulletin (Ministry of Ecology and Environment of People’s Republic of China, 2006); https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/National Radiation Monitoring (National Radiation Environment Monitoring Network, 2020); http://www.rmtc.org.cn/hjzl/jcbg/fshjzlbg/Media/Ministry Report (East China Inspection Bureau, Ministr of Ecology and Environment, 2020); https://hddc.mee.gov.cn/hdyq/zjs/202006/t20200630_786613.shtmlMedia Report: Why Zhejiang Rural Household Garbage Classification in the Forefront of the Country (Zhejiang, 2019); https://town.zjol.com.cn/cstts/201907/t20190711_10566409.shtmlLv, F., Hua, Z., Liping, H., Li-ming, S. & Pin-jing, H. Analysis on the Treatent Technology of Organic Fraction of Municipal Solid Waste in the Neighborhood or On-Site (in Chinese) (Environmental Sanitation Engineering, 2020).Download referencesAcknowledgementsWe acknowledge support from the Westlake Education Foundation and the Research Center for Industries of the Future at Westlake University (L.W.), the Zhejiang Provincial Department of Science and Technology (grant no. 2023SDXHDX0006, L.W.), and the Key Research and Development Project of Zhejiang Province, China (grant nos 2021C03024 and 2022C03001, W.W.). We thank X. Yang (at Westlake University) for his assistance in manuscript editing, the Westlake Lanuage Center for providing their services and FanRuan Software Co, Ltd 2019 for providing free service in figure drafting.Author informationAuthor notesThese authors contributed equally: Fang Liu, Liqing Xin.Authors and AffiliationsCollege of Environmental and Resource Sciences, Zhejiang University, Hangzhou, ChinaFang Liu, Liqing Xin, Hang Tang, Yong Qin& Weixiang WuKey Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, ChinaFang Liu, Lei Zhang, Xinyue Dong, Yi Zhang & Lei WangInstitute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, ChinaFang Liu, Lei Zhang, Xinyue Dong, Yi Zhang & Lei WangResearch Center for Industries of the Future, Westlake University, Hangzhou, ChinaLei WangAuthorsFang LiuView author publicationsYou can also search for this author in
PubMed Google ScholarLiqing XinView author publicationsYou can also search for this author in
PubMed Google ScholarHng TangView author publicationsYou can also search for this author in
PubMed Google ScholarYong QinView author publicationsYou can also search for this author in
PubMed Google ScholarLei ZhangView author publicationsYou can also search for this author in
PubMed Google ScholarXinyue DongView author publicationsYou can alsosearch for this author in
PubMed Google ScholarYi ZhangView author publicationsYou can also search for this author in
PubMed Google ScholarWeixiang WuView author publicationsYou can also search for this author in
PubMed Google ScholarLei WangView author publicationsYou can also search for this author in
PubMed Google ScholarContributionsF.L. and L.W. conceptualized the project and devised the methodology. F.L. managed the software, wrote the original draft of the paper and visualized the data. F.L., L.X., H.T., L.Z., X.D., Y.Z., W.W. and L.W. validated the results. F.L. and L.X. conducted the formal analysis. F.L., L.X., H.T., Y.Q., L.Z., X.. and L.W. conducted the investigation. F.L., L.X., H.T., Y.Q. and L.Z. curated the data. L.X., W.W. and L.W. revised the manuscript. W.W. and L.W. provided the resources and acquired the funding for the project. L.W. supervised the project and reviewed and edited the manuscript.Corresponding authorsCorrespondence to Weixiang Wu or Lei Wang.Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Food thanks the anonymous reviewers for their contribution to the peer review of this work.
Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary InformationSupplementary Main, Results, Methods, Figs. 1–8, Tables 1–8 and references.Reporting SummarySupplementary Data 1The assumptions, relevant pollution equivalent values, applicable taxes in 31 provinces, esimation details of the monetization method Chinatax, LCA and LCC results, monetization results in 31 provinces, and sensitivity analysis datasets (sheets 0 to 25).Source dataSource Data Fig. 2Source data for the monetization factors in ten monetization methods and for the onetization factors of ChinataxRCP in China’s 31 provinces.Source Data Fig. 3LCA results of ten rural food waste treatment technologies for nine environmental impact categories.Source Data Fig. 4Monetization results for nine rural food waste management technologies.Source Data Fig. 5Economic analysis source data and potential trade-offs between environmental and economic analysis results for rural food waste management technologies.Source Data Fig. 6Scenario analysis source data for monetization results using different substitution methods and under various policy scenarios.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing greement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and PermissionsAbout this articleCite this articleLiu, F, Xin, L., Tang, H. et al. Regionalized life-cycle monetization can support the transition to sustainable rural food waste management in China.
Nat Food 4, 797–809 (2023). https://doi.org/10.1038/s43016-023-00842-6Download citationReceived: 12 October 2022Accepted: 11 August 2023Published: 18 September 2023Issue Date: September 2023DOI: https://doi.org/10.1038/s43016-023-00842-6Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
