Cascading impacts of food loss and waste on biodiversity through agricultural land use

doi:10.21203/rs.3.rs-5048002/v1

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Cascading impacts of food loss and waste on biodiversity through agricultural land use

https://doi.org/10.21203/rs.3.rs-5048002/v1

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Food loss and waste (FLW) drives additional agricultural production and cropland expansion to meet food demand, leading to habitat loss and posing a threat to biodiversity worldwide. In this study, we traced how FLW influences agricultural land use and biodiversity through global food trade and quantified the resource-saving benefits of various FLW reduction scenarios. In 2018, approximately 2,246.3 Mt of FLW was generated, accounting for a land footprint of 800.3 Mha and leading to the equivalent of 13,920 species*year of potential global species extinction. Fast-growing economies such as China, Brazil, and Iran dominated the growth of FLW between 2000 and 2018. Of global FLW, 54.1% was generated by the top 10 countries with the highest FLW. Halving FLW in just these 10 countries or 40 biodiversity hotspots is expected to reduce FLW-related biodiversity losses by 31.1–35.1%, which is more beneficial than halving FLW in all remaining countries. The results reveal a strong yet under-discussed synergy between FLW reduction and biodiversity conservation via land transformation, highlighting where FLW intervention should be prioritized. While it is widely acknowledged that limiting cropland expansion is crucial for biodiversity conservation, we advocate for integrating FLW reduction efforts into the implementation strategy of the post-2020 Global Biodiversity Framework.

Earth and environmental sciences/Environmental sciences/Environmental impact

Scientific community and society/Agriculture

Scientific community and society/Geography

Around one-third of food is lost or wasted along the food supply chain from farm to table, posing significant challenges to the sustainable development of the global food system¹. Understanding and addressing food loss and waste (FLW) is crucial for maintaining global food security², reducing greenhouse gas emissions^3,4, and protecting the Earth’s ecosystem^5,6. The United Nations Sustainable Development Goals (SDGs) have proposed Target 12.3, which aims to halve global food waste and substantially reduce food losses by 2030⁷. In response, various interventions have been initiated worldwide to reduce FLW, such as China’s Anti-Food Waste Law⁸ and the European Green Deal aimed at fostering a circular economy⁹.

In addition to causing food shortages, FLW intensifies the strain on already limited agricultural land resources, with some land being used for compensatory production to fill the food gap^10,11. Concurrently, large tracts of natural habitats are being transformed into croplands due to escalating food demands¹². Global cropland expansion rates have nearly doubled since the beginning of the 21st century (5 Mha⋅yr^− 1), with 49% of the new cropland replacing existing natural vegetation¹³. Unfortunately, this expansion has made minimal contributions to food supply² and has led to substantial biodiversity losses^14–19. In contrast, reducing FLW can substantially increase the food supply without escalating social and environmental pressures⁶. It can also stimulate land abandonment and reduce cropland expansion^2,20, thereby protecting the biodiversity of both cultivated land²¹ and uncultivated habitats²². Therefore, reducing FLW may be a wise strategy for mitigating present and future environmental impacts of agricultural production.

With globalization and the expansion of the food trade, FLW not only affects the local environment but also has interconnected impacts on other regions through the global food trade^23–26. In 2021, FLW in Europe (EU) totaled 154 Mt, exceeding its agricultural imports of 138 Mt²⁷, with 46–74% of the environmental footprint associated with FLW coming from imported food¹⁰. This suggests that FLW in EU may drive trade partner countries to increase agricultural production but there is potential for food savings in these countries, which could simultaneously protect land resources and biodiversity. From a global perspective, the rise in import-embedded FLW in high-income countries and fast-growing economies has exacerbated the environmental impacts in low- and middle-income exporting countries by boosting exports²⁸. Countries with biodiversity hotspots, such as some tropical countries, may experience negative impacts on their ecosystems by food trades²⁹.

Due to variations in dietary habits, production efficiency, and habitat quality, the land footprint resulting from FLW¹⁰ and biodiversity loss from land transformation^30,31 differ significantly across regions. Previous research has primarily focused on the environmental footprint generated by FLW, such as blue water consumption and greenhouse gas emissions^3,6,20, or the impacts of food production and trade on biodiversity^15,32, but the relationships between FLW and biodiversity loss have not been adequately examined. As global food trade increases, identifying regions where FLW contributes most to biodiversity loss becomes an increasingly urgent but challenging task. Given that the deadline for achieving SDG Target 12.3 by 2030 is rapidly approaching and considering the diverse compositions and volumes of FLW in each country, there is a critical need to explore effective and economically feasible strategies for prioritizing FLW interventions.

In this study, we conducted a comprehensive analysis to trace the land footprint and potential biodiversity loss associated with FLW within the global food trade and evaluated the expected environmental benefits under four FLW reduction scenarios. We first compiled a country-level FLW database and quantified the trade-embedded land footprint associated with FLW by using Multi-Regional Input-Output (MRIO) tables. The characterization factors (CFs) were adopted to assess the potential biodiversity loss caused by FLW-related land transformation. The Global Trade Analysis Project (GTAP) model was used to simulate the conservation benefits for land use and biodiversity under four FLW reduction scenarios. Our approach builds on the assumption, supported by previous studies^20,33, that reducing FLW throughout the supply chains reduces the need for additional food production. Our findings reveal a synergy between SDG Target 12.3 (‘Food Loss and Waste Reductions’) and SDG Target 15.5 (‘Protect Biodiversity and Natural Habitats’). To catalyze global FLW reduction efforts, we advocate incorporating FLW reduction strategies into implementable actions for the post-2020 Global Biodiversity Framework.

Global land footprint associated with FLW

In 2018, approximately 800.3 Mha of agricultural land (i.e., land footprint) was used to produce 2,247.6 Mt of food (accounting for 22.6% of global primary production) that ultimately ended up as FLW. This substantial FLW and land footprint were predominately generated in several countries with large populations or high consumption levels (Fig. 1a,b).

From 2000 to 2018, global FLW and land footprint increased by 483.8 Mt (+ 27% compared to 2000) and 237.8 Mha (+ 42% compared to 2000) respectively, creating rapidly increasing environmental impacts^4,28, including biodiversity hotspots³³. FLW growth occurred primarily in fast-growing economies (Fig. 1c), especially Brazil (increased by 74.1 Mt, same below), China (46.7 Mt), and Iran (30.5 Mt) among BRICS countries. The situation was similar for land footprints (Fig. 1d). However, in Africa, where the growths of FLW volumes were not so severe, the increases in FLW ratios were definitely shocking (Supplementary Fig. 2). Unlike other countries where ratios of food loss and food waste generally fluctuated, most African countries have experienced increases in both ratios over the past two decades, with some countries increased by more than 5% in both. This implies that if food production in Africa had stagnated over these years, the nutritional supply would have been significantly reduced, exacerbating instability risks and increasing environmental pressure in poverty areas.

If using gross domestic product (GDP) growth rates between 2000 and 2018 as proxies for countries’ development speeds, then higher FLW increases primarily came from countries with higher GDP growth rates. (Fig. 1e). Furthermore, the top 10 countries accounted for 54.1% of the global FLW (Fig. 1f) because of their large populations and food consumption patterns. Notably, China, the United States, India, and Brazil were the main contributors, consistent with previous study⁴, collectively generating 39.2% of the global FLW. While the increase in land footprint was predominately in countries with higher GDP growth rates (Fig. 1g), countries with higher FLW volumes did not necessarily generate higher land footprints (Fig. 1h) because land footprints were closely related to food categories and production intensity. For instance, China produced the highest FLW but not the highest land footprint due to its focus on horticulture (vegetables, fruits, and nuts), which is intensive in capital and labor but less demanding in land use. The United States had the highest land footprint, accounting for 10% of the global land footprint. Australia and several African countries generated large land footprints but did not rank in the top 10 for FLW.

FLW and land footprint varied by region and dietary pattern. Horticulture dominated FLW in EU, Asia, and Africa (Fig. 1i), contributing 40.5% to the global FLW (Fig. 1j); however, it only contributed 10.2% of the global land footprint owing to its intensive operation characteristics (Fig. 1k,l). In contrast, FLW from meat and animal products resulted in substantial land footprints in most regions (60.0% of the global land footprint) because pasture was characterized by extensive operation. In addition, FLW from meat and animal products was more prevalent in regions with higher meat consumption preferences, such as Northern America and Oceania (NAM + OC)⁴, where 31.9% of FLW came from meat and animal products (Fig. 1i). The share of oil and sugar crop FLW in Latin America and the Caribbean (LAM + CAR) was approximately 52.3%, which was much higher than the global average of 25.1%. More detailed information on the 10 food contributions is provided in Supplementary Fig. 3. Overall, a few countries dominated the growth of FLW and global land footprint, which may further exacerbate related environmental issues in the future.

Land footprint flows embedded in global food trade

In 2018, approximately 15.9% of the global land footprint was associated with food trade. Virtual land flows embedded in the food trade mainly flowed from LAM + CAR and NAM + OC to Eastern Asia and South-eastern Asia (EA + SEA), Western Asia and Northern Africa (WA + NAF), and EU (Fig. 2a,b).

Countries with the highest export-embedded land footprint included the United States, Russia, Argentina, and Zimbabwe, which together covered 26.5% of the global land footprint exports (Fig. 2c). The most import-embedded land footprint countries included China, the United States, Russia, and Japan, accounting for 25.6% of the global land footprint imports.

From 2000–2018, the trade-embedded land footprint grew from 106.1 Mha to 166.5 Mha (Supplementary Fig. 4). Especially for China, which was the highest net importer of land footprints in 2018, the virtual land flows from the United States and Brazil to China together accounted for 4% of global total imports and increased by 2 and 10 times in last two decades, respectively. Its increasing FLW imports may motivate agricultural production and enhance environmental impacts for exporters. In contrast, Japan and some EU countries showed a decrease in their land footprint imports (Supplementary Fig. 4f), which may be dependent on improved food supply technologies and the implementation of anti-food waste laws³³.

Potential biodiversity loss

FLW exacerbates the challenge of meeting rising food demands by necessitating additional land transformation for compensatory production^10,11 to bridge the gap between food supply and demand. Land transformation often involves converting natural habitats into agricultural land through rapid cropland expansion^15,35. Given the long recovery period needed to restore biodiversity³⁶ and ecosystem quality³⁷ on converted land, we evaluated the associated biodiversity loss by aggregating the impact over the entire regeneration period (in species*year)³⁸ as in previous studies^35,39. Potential biodiversity loss was assessed at the regional and global scales, where regional biodiversity loss is defined as the regional extinction of non-endemic species that can be recovered by migration from other regions, and global species extinction represents an irreversible global extinction of endemic species.

Globally, the FLW-related land footprint in 2018 was estimated to have resulted in a regional species loss of 543,371 species*year (Fig. 3a) and a global species extinction of 13,920 species*year (Fig. 3b), with the greatest biodiversity loss occurring in LAM + CAR and EA + SEA.

Around two-thirds (66.3%) of the regional species loss occurred in birds, whereas amphibians, reptiles, and mammals accounted for 5.2, 12.1, and 16.3%, respectively. Comparatively, the four taxa were more evenly affected in terms of global species extinction, with birds, amphibians, reptiles, and mammals accounting for 27.4, 36.1, 20.1, and 16.4%, respectively.

Biodiversity losses were concentrated in countries with higher land footprint generation or exports. From the average perspective of these two types of biodiversity loss (Fig. 3c,d), the greatest biodiversity loss occurred in China (8.1% globally) due to its large land footprint generation, which put great pressure on domestic production, even when compensated by the highest land footprint imports. Owing to the larger land footprint bases, Mexico, India, and Indonesia accounted for 6.7, 5.8, and 4.7% of global biodiversity loss, respectively. The remaining biodiversity losses were concentrated in major exporters of land footprint, such as Brazil, the United States, and Australia. However, due to the lower species richness in high-latitude areas, biodiversity in Russia and Canada was less affected, even when their land footprint exports were relatively high.

Resources conservation in FLW reduction scenarios

While eliminating FLW may be unrealistic, targeted FLW reduction efforts can lead to significant impacts on land resources and biodiversity. To this end, four FLW intervention strategies were established (Methods), with the cascade mechanisms depicted in Supplementary Fig. 5. Generally, reducing FLW depresses domestic food production and imports but enhances exports, triggering a cascading effect that contracts global food production through the food trade, thereby stimulating land abandonment and curbing cropland expansion to conserve biodiversity.

Approximately 355.0 Mha of land footprint (44.4% globally) could not be generated if all countries could halve FLW, which roughly meets the SDG Target 12.3 (S_all; Fig. 4a).

The reduction in land footprint associated with food waste is concentrated in NAM + OC and EU, highlighting the importance of reducing food waste on the table in related countries. Comparatively, halving FLW in only the top 10 countries with the highest FLW could reduce the global land footprint by 175.4 Mha (21.9%; S_top10; Fig. 4b). The saved food can be used to more adequately meet domestic consumption and export demands, prompting other countries to boost their imports and reduce their domestic production. For instance, this would reduce Argentina’s land footprint by 35% without any domestic FLW reduction measures. The reduction of land footprint in S_top10 is primarily in areas with higher species richness or biomass, leading to a reduction in biodiversity loss of 33.2–34.7% (Fig. 4e), which is more than half of the conservation benefits in S_all.

Meat and animal products accounted for a relatively low share of global FLW. Thus, halving meat and animal product FLW (Smeat; Fig. 4c) could only require a 7.0% reduction in global FLW but result in a 30.7% land footprint reduction, making it quite cost-effective in terms of land conservation. Furthermore, the land-saving benefit of halving FLW in 40 biodiversity hotspot countries (S_hotspot; Fig. 4d) was between S_top10 and S_meat but its biodiversity conservation benefit was much closer to S_top10. This means that even when only the top 10 countries or biodiversity hotspots halve FLW, the conservation effect on biodiversity will be greater than the total efforts of the remaining countries to halve FLW.

Due to the extremely high FLW volume, China was the country with the highest biodiversity conservation benefits in all scenarios, accounting for an average of 23.8% of regional species loss (Supplementary Fig. 6) and 19.0% of global species extinction (Supplementary Fig. 7). In addition, India, Japan, and the Philippines also experienced significant reductions in biodiversity losses from scenarios other than S_meat, resulting in higher biodiversity conservation benefits in EA + SEA (Supplementary Fig. 8). Meanwhile, the sharp decline in food exports would greatly alleviate potential species extinction in LAM + CAR, which could ease pressure on biodiversity hotspots in South America that are experiencing threats from cropland expansion.

This study linked the FLW and potential biodiversity loss from a land footprint perspective and quantitatively compared the resource-saving effects through targeted FLW reduction strategies, thus revealing a synergy between achieving SDG Target 12.3 (‘Food Loss and Waste Reductions’) and SDG Target 15.5 (‘Protect Biodiversity and Natural Habitats’) and validating the results of previous studies^33,40.

Owing to variations in production efficiency, infrastructure construction, and climatic conditions, regions differ in the land area required to produce equivalent amounts of food. For regions like Sub-Saharan Africa (SSAF) with lower agricultural production efficiency, more land is needed to compensate for food losses during production to fill the food gap. Intervention strategies, such as technological improvements and intensification, should be adopted to mitigate potential biodiversity losses, particularly in tropical regions rich in species diversity, where preserving agricultural land offers significant benefits for biodiversity conservation.

Regions with higher consumption levels, such as NAM + OC and EU, necessitate strategies focused on reducing waste. Similarly, fast-growing economies also experience increased food waste as consumption levels rise (Supplementary Fig. 2c), highlighting the need for preemptive anti-food waste legislation. Fortunately, laws against FLW have been formulated for developed countries and large contributors to FLW, including the United States, France, China, and Japan.

A decline in land footprint imports has been observed in high-income countries such as Japan, Germany, and Iceland (Supplementary Fig. 4f). It should be noted that even in countries with substantial FLW but relatively low per capita levels, such as China, implementing modest FLW reduction strategies per person can yield significant overall benefits.

With the deadline for SDG Target 12.3 approaching in 2030, countries with higher FLW should prioritize developing and strictly implementing plans to reduce FLW, which is key to both ensuring global food security and reducing agriculture-related biodiversity loss. Although establishing a transnational system to regulate global FLW may be quite difficult, reducing FLW through improved harvesting and storage techniques and standardized dietary habits may be more feasible for fast-growing economies and developed countries, which are the main contributors to global FLW. If implementation remains resistant, policies should prioritize reducing meat and animal product FLW as this will conserve the most land resources and biodiversity with the least FLW reduction. Furthermore, encouraging a greater inclusion of plant-based foods in the diet and recycling food waste as animal feed can further conserve environmental resources⁴¹.

Although many countries with biodiversity hotspots have shifted their roles in global food trade primarily from net exporters to net importers in recent years³², the benefits in S_hotspot demonstrate that FLW reduction efforts in these countries remain indispensable. Especially for food exporting countries with biodiversity hotspots, controlling food losses at the farm-warehouse-factory level may be a priority. For example, the growth in soybean exports in Brazil’s Atlantic and Cerrado hotspots has posed a severe threat to local biodiversity⁴². In addition, major importers of countries with biodiversity hotspots should also prioritize FLW reduction measures. In Mexico, feeding the country’s projected population of 146 million by 2050 while meeting biodiversity targets would require doubling beef and milk imports even as domestic productivity rises⁴³. Incorporating biodiversity production costs into food prices⁴⁴ is expected to limit overconsumption of the importers, such as the excessive consumption of meat in the United States⁴⁵.

Future research could integrate the findings from this study with hotspot events or targeted regions to further optimize the intervention strategies. It should be emphasized that food conservation is a global responsibility and not just for hotspots or high FLW countries. The designed intervention scenarios from this study only provide information for maximizing the benefits of land and biodiversity conservation with limited efforts. Considering the variability in threats posed by agricultural production based on location²⁵, future research could benefit from employing high-resolution data within national boundaries to identify which agricultural lands are more susceptible to abandonment because of FLW reduction. Furthermore, it could determine which natural habitats might remain uncultivated as a result of FLW reduction, thus allowing for a more comprehensive assessment of the cascading impacts of agricultural production, FLW, and biodiversity^46,47. We also recommend incorporating FLW reduction into relevant plans and actions within the global biodiversity conservation framework to ensure that its ecological benefits receive attention.

This study had the following limitations: First, changes in agricultural production resulting from FLW reduction can impact both the area and intensity of production, with changes in production intensity also affecting biodiversity outcomes²¹. However, our analyses only considered changes in the production area at a national scale. Future research would benefit from incorporating relevant datasets with higher spatial resolution to accurately capture regional variations. Second, because of the limited FLW data available from FAO, updating FLW data beyond 2018 is subject to greater uncertainty. It is possible that responses to SDG Target 12.3, particularly in countries such as the UK and the Netherlands⁷, have led to improvements that are more favorable than our simulation results. Third, while calculating biodiversity losses associated with meat and animal product FLW, forage fields associated with livestock feeding were not distinguished from pastures, which may lead to an underestimation of biodiversity losses. Fourth, the national-scale average CFs are used as proxies, which could not fully reflect the actual biodiversity loss risk because biodiversity is not evenly distributed within individual countries. Fifth, we assessed potential rather than actual biodiversity loss caused by FLW. We assumed that reducing FLW could offset some of the pressure on biodiversity from cropland expansion; however, other factors that may cause biodiversity loss, such as climate change, were not considered. Despite these limitations, our assessment contributes to the understanding between FLW and biodiversity loss via land transformation and highlights areas where biodiversity is particularly vulnerable to FLW impacts, emphasizing the importance of integrating FLW reduction into broader biodiversity conservation efforts.

This study traced the impacts of FLW on agricultural land use and biodiversity through the global food trade and quantified the resource-saving benefits across various FLW reduction scenarios. In 2018, approximately 2,246.3 Mt of FLW was generated, associated with 800.3 Mha of land footprint and led to the equivalent of 13,920 species*year of potential global species extinction. China, Mexico, and India suffered the greatest biodiversity loss due to their higher FLW generation. From 2000 to 2018, FLW growth was dominated by fast-growing economies such as China, Brazil, and Iran. Of global FLW, 54.1% is concentrated in 10 countries with large populations or high consumption levels. Halving FLW in just these 10 countries or the 40 countries with biodiversity hotspots is expected to reduce FLW-related biodiversity losses by 31.1–35.1%, which is more beneficial than halving FLW in all remaining countries. Halving meat and animal product FLW should be a priority measure because it only requires a 7.0% reduction in FLW to mitigate the land footprint by 30.7% and biodiversity loss by 21–22.5%.

The findings underscore where FLW reduction efforts should be strategically targeted or prioritized to conserve significant land resources and biodiversity within a limited time. Moreover, they highlight the synergy between SDG Target 12.3 (‘Food Loss and Waste Reductions’) and SDG Target 15.5 (‘Protect Biodiversity and Natural Habitats’). The study advocates for incentivizing FLW reduction efforts by incorporating them into the actions outlined in the post-2020 Global Biodiversity Framework.

Global FLW data

A new FLW database was established by combining the FAO database and literature reviews in this study. First, we derived the food production data from FAOSTAT⁴⁸. Due to regional differences in food production, only 174 countries with food production data in both 2000 and 2018 were selected in this study. However, for countries where data on specific food categories were missing, the average production of the same food categories of all the other countries in the same geographical region was adopted, with replacement rates of missing values of 3.5%, 24.2%, 13.2%, 8.4%, 9.3%, 7.5%, and 7.2% for NAM + OC, EU, WA + NAF, LAM + CAR, Central Asia + Southern Asia (CA + SA), EA + SEA, and SSAF, respectively. The food production data was then fed into the MRIO framework to quantify the food consumption of each country, which was calculated by subtracting exports from production and adding imports.

Second, the FLW data in percentages from the Food Loss and Waste Dataset provided by FAO⁴⁹ and previous review^28,50 were extracted. Due to the lack of FLW data, we classified the reduction in food mass from primary production to manufacturing as food loss, and the reduction from distribution to consumption as food waste. A four-year window was set up to calculate the average values from 1996–2004 and 2014–2022 as the FLW data for 2000 and 2018, respectively. Since the FLW datasets provided by Gatto and Chepeliev²⁸ only have data from 2000 and 2014 and are in weight units, we divided the datasets by the food consumption in 2000 and 2014 respectively, and obtained data in percentages. Finally, the FLW percentage data were multiplied by the food consumption data to obtain the FLW database (in million tonnes).

The FLW database provides physical flows of losses and waste of 10 food categories, including paddy rice, wheat, other grains, other crops, horticulture, oil seeds, sugar beets and sugar canes, meat, other animal products, and raw milk along food supply chains in 174 countries and regions for 2000 and 2018. For a detailed list of foods, please refer to https://www.gtap.agecon.purdue.edu/databases/contribute/detailedsector.asp.

A country’s FLW from domestic consumption may originate from both domestically produced and imported food, and similarly, food produced in that country may be wasted by other countries through exports²⁸. Therefore, the FLW generated from domestic production can be calculated as the FLW generated from domestic consumption minus imported FLW plus exported FLW:

$$\:\begin{array}{c}{\text{F}\text{L}\text{W}}_{i,prod}^{r}={\text{F}\text{L}\text{W}}_{i}^{r}+\sum\:_{s}^{174}{\text{F}\text{L}\text{W}}_{i}^{r,s}-\sum\:_{t}^{174}{\text{F}\text{L}\text{W}}_{i}^{t,r} \:\left(1\right)\end{array}$$

$$\:\begin{array}{c}{\text{F}\text{L}\text{W}}_{prod}^{r}=\sum\:_{i}^{10}{\text{F}\text{L}\text{W}}_{i}^{r} \left(2\right)\end{array}$$

where $\:{\text{F}\text{L}\text{W}}_{i,prod}^{r}$ is the FLW generated from domestic production by sector $\:i$ in region $\:r$; $\:{\text{F}\text{L}\text{W}}_{i}^{r}$ is the FLW generated from domestic consumption by sector $\:i$ in region $\:r$; $\:{\text{F}\text{L}\text{W}}_{i}^{r,s}$ is the trade-embedded FLW flows from region$\:\:r\:$to $\:\text{s}$; $\:{\text{F}\text{L}\text{W}}_{i}^{t,r}$ is the trade-embedded FLW flows from region$\:\:t\:$to $\:\text{r}$; and $\:{\text{F}\text{L}\text{W}}_{prod}^{r}$ is the total FLW generated from domestic production in region $\:r$.

MRIO analysis

Two MRIO tables^51,52 were integrated to quantify the land footprint generated by FLW along the food supply chains. As the most commonly used MRIO databases have a low-level classification of countries (EXIOBASE⁵³ only classifies 44 countries and regions) or food production sectors (Eura26⁵⁴ has a coarse agricultural classification), we used a high-resolution (189 countries×163 sectors) MRIO table⁵¹ compiled from the EXIOBASE, Eura26, and FAO datasets and selected data for 2000 and the latest year 2015.

Because the MRIO table only provides value-based monetary flows, we referred to previous studies^55,56 to link it with the physical Food and Agriculture Biomass Input-Output datasets⁵², obtained the material flow of food production in physical equivalents (tonnes), and calculated the life cycle land footprint per unit of food equivalent³⁵:

$$\:\begin{array}{c}Land\:=\:{\mathbf{D}\left(\mathbf{I}-\mathbf{A}\right)}^{-1} \left(3\right)\end{array}$$

where$\:\:\mathbf{L}\mathbf{a}\mathbf{n}\mathbf{d}\:$is the life cycle land footprint coefficient matrix. The matrix is composed of $\:{land}_{i}^{r}$, which is the land footprint per unit of FLW generated by sector $\:i$ in region $\:r$;$\:\:\mathbf{D}\:$is the direct land footprint coefficient matrix consisting of $\:{d}_{i}^{r}$;$\:\:\mathbf{I}\:$is an identify matrix; and$\:\:\mathbf{A}\:$is the direct consumption coefficient matrix consisting of $\:{a}_{i,j}^{r,s}$, which is the direct consumption of sector$\:\:j\:$in region$\:\:s\:$by sector$\:\:i\:$in region$\:\:r$. Due to differences in data statistics, data with production less than 1,000 tonnes were discarded, and the extreme values of land use coefficients were limited to 20% and 80%, respectively. In addition, land use factors of other animal products were replaced by that of raw milk due to missing data.

Virtual land flows were calculated using the following equation:

$$\:\begin{array}{c}{\text{f}\text{l}\text{o}\text{w}}^{r,s}=\sum\:_{i}^{10}{\text{l}\text{a}\text{n}\text{d}}_{i}^{r}\times\:{\text{y}}_{i}^{r,s} \left(4\right)\end{array}$$

where $\:{\text{f}\text{l}\text{o}\text{w}}^{r,s}$ represents the virtual land flows from region$\:\:r\:$to $\:\text{s}$; and $\:{\text{y}}_{i}^{r,s}$ is the final use of commodity $\:i$ produced in region $\:r$ and finally used by region $\:s$.

The land footprint generated from domestic production was calculated using the following equation:

$$\:\begin{array}{c}{\text{L}\text{F}}_{prod}^{r}=\sum\:_{i}^{10}{\text{l}\text{a}\text{n}\text{d}}_{i}^{r}\times\:{\text{F}\text{L}\text{W}}_{i,prod}^{r} \left(5\right)\end{array}$$

where $\:{\text{L}\text{F}}_{prod}^{r}$ is the total land footprint generated from domestic production in region $\:r$; $\:{\text{l}\text{a}\text{n}\text{d}}_{i}^{r}$ is the land footprint per unit of FLW generated by sector $\:i$ in region $\:r$; $\:{\text{F}\text{L}\text{W}}_{i,prod}^{r}$ is the FLW generated from domestic production by sector $\:i$ in region $\:r$.

GTAP model

The GTAP model^57,58 was used to evaluate the effect of FLW reduction in specific countries on a global production scale. The GTAP is a multi-regional and multi-sector model built on computable general equilibrium theory. Based on the established input-output framework, the model provides a bilateral matrix of global food trade in the form of monetary flows, which can trace where primary production and final consumption occur. As a comparative static model, the GTAP captures market-mediated responses under different policy scenarios through changes in supply-demand and participant behavior and is therefore widely used to simulate and assess the impacts of specific events on the global food trade^35,59–61.

This study used the latest publicly available GTAP 10 database^57,58 and followed the default classification of 141 countries and regions and five primary production factors, with sectors reclassified into 10 agricultural sectors and one non-agricultural sector to match the FLW datasets. The agricultural sectors include paddy rice, wheat, other grains, horticulture, oil seeds, sugar beets and sugar canes, other crops, meat, other animal products, and raw milk.

We simulated the coupled impacts of different FLW intervention strategies on multi-regional and multi-sector by setting shocks in the model. Because the GTAP model does not directly provide exogenous variables for FLW changes and previous studies^35,62 used technologies of food production and transportation as proxies for production changes, we used ‘intermediate input product technology’ to reflect the improved utilization efficiency due to FLW reduction. The GTAP output was merged with the FLW database and fed into the MRIO models.

Biodiversity loss assessment

The CFs³⁹ based on life cycle assessment (LCA) were employed to quantify the land transformation-related biodiversity loss. Unlike the sharp decline in biodiversity when a habitat is destroyed, the recovery of biodiversity to its original state requires a relatively long regeneration period, which is determined by the degree of damage and recovery capacity of the ecosystem^39,63. Considering the long-term comprehensive impacts of land transformation on biodiversity⁶⁴, the LCA assessment scope of biodiversity includes the number of species and regeneration period, which are multiplied to provide a quantitative indicator of biodiversity. Given that the loss of endemic species is comprehensive and irreversible, we measured both regional species loss and global species extinction based on a previous study³⁵.

The CFs³⁹ calculated using the average approach provide average threat coefficients for mammals, birds, reptiles, and amphibians for six land use types in 245 countries and regions. The impacts of meat and animal product FLW were distinguished by using CFs from pastures. The actual land types in the transformation could not be determined because the land footprint generated by FLW is a virtual cultivated land flow; therefore, we uniformly assumed that the natural habitat was extensive forestry and did not include grassland with lower species richness, which may lead to an overestimation of the biodiversity loss⁶⁵ caused by FLW:

$$\:\begin{array}{c}{\text{l}\text{o}\text{s}\text{s}}_{crop}^{r}=\sum\:_{i}^{4}{(\text{C}\text{F}}_{i,cropland}^{r,loss}-{\text{C}\text{F}}_{i,forest}^{r,loss})\times\:{\text{L}\text{F}}_{prod,crop}^{r} \left(6\right)\end{array}$$

$$\:\begin{array}{c}{\text{l}\text{o}\text{s}\text{s}}_{meat}^{r}=\sum\:_{i}^{4}{(\text{C}\text{F}}_{i,pasture}^{r,loss}-{\text{C}\text{F}}_{i,forest}^{r,loss})\times\:{\text{L}\text{F}}_{prod,meat}^{r} \left(7\right)\end{array}$$

where $\:{\text{l}\text{o}\text{s}\text{s}}_{crop}^{r}$ and $\:{\text{l}\text{o}\text{s}\text{s}}_{meat}^{r}$ are regional species losses in region $\:r$ caused by cropland and pasture transformation, respectively; $\:{\text{C}\text{F}}_{i,cropland}^{r,loss}$, $\:{\text{C}\text{F}}_{i,pasture}^{r,loss}$, and $\:{\text{C}\text{F}}_{i,forest}^{r,loss}$ are CFs of regional species loss of taxa $\:i$ in cropland, pasture, and forest in region $\:r$, respectively; and $\:{\text{L}\text{F}}_{prod,meat}^{r}$ and $\:{\text{L}\text{F}}_{prod,crop}^{r}$ are the land footprints generated from the domestic production of meat and animal products and other food categories in region $\:r$, respectively.

For global species extinction caused by FLW, the following formulas were used:

$$\:\begin{array}{c}{\text{e}\text{x}\text{t}\text{i}\text{n}\text{c}\text{t}\text{i}\text{o}\text{n}}_{crop}^{r}=\sum\:_{i}^{4}{(\text{C}\text{F}}_{i,cropland}^{r,extinction}-{\text{C}\text{F}}_{i,forest}^{r,extinction})\times\:{\text{L}\text{F}}_{prod,crop}^{r} \left(8\right)\end{array}$$

$$\:\begin{array}{c}{\text{e}\text{x}\text{t}\text{i}\text{n}\text{c}\text{t}\text{i}\text{o}\text{n}}_{meat}^{r}=\sum\:_{i}^{4}{(\text{C}\text{F}}_{i,pasture}^{r,extinction}-{\text{C}\text{F}}_{i,forest}^{r,extinction})\times\:{\text{L}\text{F}}_{prod,meat}^{r} \left(9\right)\end{array}$$

where $\:{\text{e}\text{x}\text{t}\text{i}\text{n}\text{c}\text{t}\text{i}\text{o}\text{n}}_{crop}^{r}$ and $\:{\text{e}\text{x}\text{t}\text{i}\text{n}\text{c}\text{t}\text{i}\text{o}\text{n}}_{meat}^{r}$ are the global species extinctions in region $\:r$ caused by cropland and pasture transformations, respectively.

Scenario design

The scenario design is based on two basic assumptions: (1) a reduction in FLW can meet food demand more effectively, which affects the scale of domestic food production and trade and has coupled impacts on other countries through the global food market²⁸; (2) if less production is needed, then less habitat will be reclaimed. A reduction in production demand may simultaneously stimulate land abandonment and inhibit cropland expansion^2,20, thereby protecting biodiversity in both cultivated land²¹ and uncultivated habitats²². Supplementary Fig. 5 illustrates this cascade mechanism.

Based on these two assumptions, we considered four interventions for FLW reduction and calculated the associated changes in global food trade and environmental impacts: (1) halving FLW for all countries, roughly meeting the UN SDG Target 12.3⁷ (S_all); (2) halving FLW for the top ten countries with highest FLW (S_top10); (3) halving meat and animal production FLW (S_meat); and (4) halving FLW for 40 countries with biodiversity hotspots (S_hotspot). The countries involved in S_top10 and S_hotspot are shown in Supplementary Fig. 9. The 2018 FLW statistic was used as a baseline based on previous studies^4,20,35.

The biodiversity hotspots in S_hotspot represent habitats in which endemic species are concentrated and endangered⁶⁶. The biodiversity hotspots map⁶⁷ used in this study met two criteria: (1) unique and rich in biodiversity (containing at least 1,500 species of vascular plants found nowhere else on Earth) and (2) endangered (at least 70% of the original native vegetation has been lost). We selected 40 countries that cover only 18.6% of the Earth’s surface but cover the majority of biodiversity hotspots.

Data availability

The production data obtained from FAOSTAT is available at https://www.fao.org/faostat/en/#home. The FAO FLW database is available at https://www.fao.org/platform-food-loss-waste/flw-data/en/. The high-resolution MRIO table can be found at https://zenodo.org/records/3993659. The FABIO database is available at https://zenodo.org/records/2577067. GDP data is obtained from https://data.worldbank.org/indicator/NY.GDP.MKTP.CD. Abbreviations and country lists of geographical regions can be found at https://unstats.un.org/unsd/methodology/m49/. The datasets of characterization factors adopted to evaluate potential biodiversity loss are available in supporting information from previous research at https://pubs.acs.org/doi/10.1021/acs.est.5b02507. The biodiversity hotspot map is available at https://zenodo.org/records/4311850. All results data of this study are available in Supplementary Information.

Acknowledgments

We would like to thank Editage for English language editing. We acknowledge support from the National Natural Sciences Foundation of China (Key Program) (41930757) and from the National Natural Science Foundation of China (General Program) (42171259).

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Cascading impacts of food loss and waste on biodiversity through agricultural land use

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Abstract

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Introduction

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Global land footprint associated with FLW

Land footprint flows embedded in global food trade

Potential biodiversity loss

Resources conservation in FLW reduction scenarios

Discussion

Conclusion

Methods

Global FLW data

MRIO analysis

GTAP model

Biodiversity loss assessment

Scenario design

Declarations

Data availability

Acknowledgments

References

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