Philippa Kaur

EDITORIAL
26 October 2021

The answer to the biodiversity crisis is not more debt

Funding pledges from China and other countries need to be given in grants — which must include research grants — and not as a reward for taking out loans.

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The Alichur Valley in Tajikistan is among a number of ecologically sensitive areas that researchers say could be affected by China’s Belt and Road Initiative.Credit: Alamy

Funding for biodiversity is getting some attention at last.In September, nine philanthropic organizations, most of them in the United States, pledged a total of US$5 billion over a decade towards projects that will help to preserve the richness of Earth’s species.This month, Chinese President Xi Jinping announced the allocation of 1.5 billion yuan ($235 million) to the new Kunming Biodiversity Fund. This will have a goal of funding projects, such as protected areas, that will contribute to slowing down and eventually reversing the loss of species and ecosystems.More details are awaited from China, along with further information on a promise made by the European Union to double its funding for biodiversity. Contributions to the Kunming fund should be given as grants, not loans; they should have a research component; and they should be pooled and managed through international organizations. Moreover, the rules for access need to be transparent and fair to all applicants. These are important factors to emphasize, because there seems to be a trend towards providing environmental finance as loans — many of them to some of the world’s poorest countries, which are often already highly indebted.
The broken $100-billion promise of climate finance – and how to fix it
The pledges were timed to coincide with the first part of the China-hosted United Nations biodiversity conference, COP15, which ended on 24 October. Collectively, the sums, although not insignificant, will amount to little more than a 1–2% increase on the roughly $133 billion a year that the world currently spends on biodiversity. Well over half of this is spent by China, the EU, Japan and the United States.Spending on biodiversity needs to increase in all regions, according to a report by the UN Environment Programme, published in May (see go.nature.com/3ekaopk). For comparison, money earmarked for tackling climate change totalled $632 billion per year in 2019–20, according to a Nature analysis (Nature 598, 400–402; 2021).The reasons that finance for biodiversity is lower than that for its climate cousin include a relative dearth of finance in low- and middle-income countries and the fact that more than half of all climate funds take the form of loans. Both public and private investors know that in financing projects such as solar energy plants or batteries research and development, they will probably see a return on their investments. By contrast, protecting a watershed or a wetland is more of a public service — and so is more likely to be funded from taxation. Partly as a result, some 86% of biodiversity funding currently comes from public sources, in the form of grants.But that might be about to change. Researchers, corporations, bankers and policymakers have been exploring how to create financial investment products — from both private and public sources — in biodiversity, as well as how to better protect nature from the negative environmental impacts of big infrastructure projects. Most industrial sectors rely on biodiversity to some extent. Food producers, forestry, clothing manufacturers and hydropower, for example, would all struggle without healthy soils, pollinators or predictable water supplies. If nature continues to degrade, the world’s economic output will begin to suffer sooner or later.
Global climate action needs trusted finance data
One idea being studied is how to create an internationally agreed reporting system so that any entity — a bank, a government or a corporation — would need to publish data on whether its investments could lead to ecological damage. Such disclosures would probably prompt financiers to think twice before taking on investments that might be environmentally harmful. Earlier this year, an organization called Taskforce on Nature-related Financial Disclosures began work to develop such a system. It is co-chaired by Elizabeth Mrema, the executive secretary of the UN biodiversity convention secretariat, and is based in Montreal, Canada.Another idea under study is called Nature Performance Bonds (NPBs). According to this model, indebted countries would be eligible for more-favourable loan repayment terms if they could commit to spending the cash saved on environmental protection.Last month, a study commissioned by the China Council for International Cooperation on Environment and Development, an organization of policymakers that advises China’s government, recommended that China become a global leader in NPBs (see go.nature.com/3pekzk7). The study says that some 52 low- and middle-income countries owe China a combined total of more than $100 billion in loans. These include loans for projects that are part of China’s Belt and Road Initiative (BRI) to upgrade energy sources, roads, railways and airports, mainly in low- and middle-income countries. Many of China’s BRI investments are in ecologically sensitive areas.The terms of China’s $235-million biodiversity announcement have not yet been confirmed. But it would be wise if this funding were not linked to the debts of countries whose biodiversity is being affected by BRI projects. Otherwise it would seem that China’s main motivation is the greening of its own investments, when, as the host of COP15, it needs to think and act more globally, and work towards creating a fund by and for all nations.
Where climate cash is flowing and why it’s not enough
The Kunming Biodiversity Fund needs to be a stand-alone grant fund, ideally managed by a mechanism involving all countries, and with transparent rules of access. It also needs to have a dedicated research component — something that is not possible through loan finance. And other nations must contribute.The need for research funding is especially acute. There are often few funding opportunities from national research bodies for researchers in low- and middle-income countries that are rich in biodiversity. The UN’s official biodiversity funder, the Global Environment Facility, based in Washington DC, does not have a dedicated research facility. It does fund some science, but that is a part of a small-grants programme (see go.nature.com/3mgu8io) that is mainly focused on funding for conservation.It is clear that biodiversity will be getting more finance. But loan finance must not crowd out or replace grant funding. There is a precedent for this. It is already happening in climate finance, for which a much-delayed $100 billion pledged to be provided annually to low- and middle-income countries will be mainly in the form of loans.A step change in biodiversity finance is needed and the Kunming Biodiversity Fund will be a welcome move in the right direction. But it will be inequitable if most of the promised finance ends up committed to loans. Finding an answer to the biodiversity crisis should not mean the poorest countries having to take on yet more debt.

Nature 598, 539-540 (2021)
doi: https://doi.org/10.1038/d41586-021-02891-y

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Crop production, water productivity, and virtual waterA method to calculate the water needed for crops is the water footprint (WF). The WF has a color-based classification: green water (precipitation), blue water (ground and surface water), and grey water (water to dilute polluted water to accepted water quality standards). A manual on how to calculate WFs has been published12. Calculations of WFs integrate green and blue crop water use (evapotranspiration by crops) over the growing period of specific crops and express results per unit of yield (m3 kg−1). The difference between crop-water use and effective rainfall is applied as a proxy for blue WFs when no data on actual irrigation water supply are available. WFs of specific crops vary greatly among countries, and even within countries45. This means that water can be saved when crops are smartly traded. This may also be possible within a country if crops are grown where water productivity is the highest.Calculation of the water footprintWater footprints (WFs) are calculated as green and blue water footprints (WFgreen, WFblue, respectively) adopting the method from the WF manual12. This study assumes that the difference between crop water requirement and evapotranspiration of green water (ETGreen) in crops is equal to the evapotranspiration of blue water (ETblue); therefore, crop water requirements are met with irrigation water. The crop water requirements are estimated with the Food and Agriculture Organization’s CROPWAT model46. The selected methods for calculating the reference evapotranspiration (ET0) and effective precipitation (Peff) are the FAO Penman–Monteith method47,48 and the USDA’s SCS method48, respectively. Calculations were performed at the provincial scale for each crop. Equations (1) through (4) are applied to calculate WFgreen and WFblue for the crops included in this study:Actual crop evapotranspiration from reference evapotranspiration:$$ ET_{c} = sum_{t} {ET_{0} times K_{c} } $$
(1)
Reference evapotranspiration:$$ ET_{0} = frac{{0.408Delta left( {R_{n} – G} right) + gamma frac{900}{{T + 273}}U_{2} left( {e_{a} – e_{d} } right)}}{{Delta + gamma left( {1 + 0.34U_{2} } right)}} $$
(2)
$$ WF_{green} = 10 times frac{{min left[ {ET_{c} ,P_{eff} } right]}}{Y} $$
(3)
$$ WF_{blue} = 10 times frac{{max left[ {0,ET_{c} – P_{eff} } right]}}{Y} $$
(4)
where ETc denotes the actual crop evapotranspiration (mm) during the growth period (t), ET0 represents the reference evapotranspiration (mm day−1), and Kc denotes the crop coefficient based on crop type and development stages (initial, middle, and late stages). In Eq. (2) ea (kPa), ed (kPa), Δ (kPa °C−1), G (MJ m−2 day−1), T (°C), Rn (MJ m−2 day−1), U2 (m s−1), and γ (kPa °C−1) denote the saturation vapor pressure, the actual vapor pressure, the slope of the saturation-vapor pressure curve, the soil heat flux, the average air temperature, the net radiation on the crop surface, the wind speed measured at a height of 2 m above ground level, and the psychrometric constant, respectively. Equations (3) and (4) calculate the green and blue water footprints (m3 ton−1), in which Peff (mm), Y (ton ha−1), and 10, are represent effective precipitation, the crop yield, and a conversion factor from mm to m3 ha−1, respectively. WFgreen and WFblue occur in irrigated cultivation; however, there is only WFgreen in rainfed cultivation.Optimization of crop productionAll the steps of the methods used in this work were coded in MATLAB for use by decision-makers, planners, and interested organizations.Balancing the agricultural systemAn internal trade network was created to organize and remedy the weaknesses of the trade network. The lack of a comprehensive trade network has caused the crops to be exported regardless of the country’s demands, which eventually leads to the import of the same crops. The production and demand amounts of each crop in each province and their WFgreen and WFblue are determined with the following equations applied to N = 51 crops in J = 31 provinces:$$ {CP}_{(i,j)} = {ICP}_{(i,j)} + {RCP}_{(i,j)} $$
(5)
$$ {ICP}_{(i,j)} = left( {{BCY}_{(i,j)} times {ICA}_{(i,j)} } right) $$
(6)
$$ {RCP}_{(i,j)} = left( {{GCY}_{(i,j)} times {RCA}_{(i,j)} } right) $$
(7)
$$ {CD}_{(i,j)} = left( {{PCD}_{i} times {POP}_{J} } right) $$
(8)
$$ {TWF}_{blue(i,j)} = {ICP}_{(i,j)} times {WF}_{blue(i,j)} $$
(9)
$$ {TWF}_{green(i,j)} = {ICP}_{(i,j)} times {WF}_{green(i,j)} $$
(10)
where (i=1, 2,ldots , N;j=1, 2, ldots, J,) CP(i,j) (ton), ICP(i,j) (ton), RCP(i,j) (ton), BCY(i,j) (ton.ha−1), GCY(i,j) (ton.ha−1), ICA(i,j) (ha), RCA(i,j) (ha), CD(i,j) (ton), PCDi (ton.person−1), POPj (person), TWFblue(i,j) (m3), and TWFgreen(i,j) (m3) denote the production of crop i in province j, crop production of irrigated land, crop production in rainfed cultivation, irrigated crop yield, rainfed crop yield, irrigated acreage, rainfed areas acreage, demand for crop i in province j, per capita diet, population of province j, the blue WF of crop i in province j corresponding to irrigated cultivation, and the green WFs of crop i in province j corresponding to irrigated cultivation, respectively.TWFblue(i,j) equals zero in rainfed cultivation, and TWFgreen(i,j) is calculated with Eq. (10) based on RCP(i,j). The deficit or surplus over the demand of the provinces were determined by comparing CP(i,j) and CD(i,j) for each crop in each province. Equation (11) implies that CS(i,j) is the amount of crop i supplied in province j (ton), which involves the export and import of crops:$$ {CS}_{(i,j)} = {CP}_{(i,j)} – {CD}_{(i,j)} $$
(11)
where (i=1, 2,ldots , N;j=1, 2, ldots, J) .The internal trade network is formed once the deficit and surplus for each crop in the provinces is determined, and crops are traded based on the shortest distance between the provinces. The developed trade network would improve the country’s agricultural system and reduce transportation costs between the provinces. Each province adds to or subtracts Ti,j (ton) from its crop amounts, where imports imply an addition and exports a subtraction of crop amounts. The internal exports and imports of WFs and the net water footprints trade (NWFT) in each province are calculated as follows:$$ {WFT}_{(x,r,i)} = T_{(x,r,i)} times left( {{WF}_{green} + {WF}_{blue} } right)_{(x,i)} $$
(12)
$$EW{F}_{(x)}={sum }_{r,i}WF{T}_{(x,r,i)}$$
(13)
$$ IWF_{(r)} = sumlimits_{x,i} {WFT_{(x,r,i)} } $$
(14)
$$ {NWFT}_{(j)} = IWF_{(j)} – EWF_{(j)} $$
(15)
where (i=1, 2,ldots , N;j, x=1, 2, ldots, J, r=x-1), WFT(x,r,i) (m3), T(x,r,i) (ton), (WFgreen + WFblue)(x,i) (m3 ton−1), EWF(x) (m3), IWF(r) (m3), and NWFT(j) (m3) denote the WFs traded for crop i from exporting province x to importing province r, the amount of crop i exported from province x to province r, the blue and green WFs related to crop i in exporting province x, the WFs exported from province x by the trade of crops, the WFs imported into province r by the trade of crops, and the net water footprints trade in province j, respectively.The positive and negative values ​​of NWFT(j) represent the import and export of WFs to province j, respectively. The calculation of the internal trade between provinces with Eq. (11) permits determining the deficits and surpluses for each crop in the provinces nationally. At this juncture the provinces may resort to international trade to cope with deficits and surpluses. However, from this work’s premise of improving food security and self-sufficiency the cropping patterns of surplus crops in the provinces are modified as described in the next section.Modifying exports to optimize the cropping patternThe multi-objective optimization approach to increase food security and self-sufficiency redirects the resources to be used to cultivate export crops to the cultivation of crops that are in deficit (i.e., whose production is less than demand). This modification of cropping patterns in the provinces is based on their traditional cropping patterns. For this purpose, the internal trade network is linked to the optimization method to manage cropping patterns of the regions based on the output of the trade network, and on the goals of achieving food security and preventing water crisis. These two goals are pertinent in many countries where water scarcity is a limiting factor to achieve food security49. Therefore, concerning available agricultural water it is imperative to pay attention to the type of water (green or blue) used. Specifically, WFblue can be used in several areas of consumption; however, WFgreen is not controllable in the same manner. The usage of WFgreen by crops depends on the growing season, and the maximum use can be achieved by choosing the optimal crops. Therefore, this work treats WFgreen and WFblue as indicators of water crisis and food security, which were chosen as objective functions. In other words, controlling and managing WFs prevent its waste (thus reducing the water deficit and crisis). Selecting optimal crops based on WFs will increase production and food security. The water crisis and food security serve as the benchmark for comparison between the reference situation (without optimization) and the results of this new method. The reference situation refers to the initial state of food security and water crisis, which occurs before optimizing the cropping patterns.The food-security objective function is expressed as follows:$$F{S}_{i}=frac{{sum }_{j=1}^{J}C{P}_{(i,j)}}{{sum }_{j=1}^{J}C{D}_{(i,j)}}$$
(16)
The water-crisis objective function is written as follows:$${WC}_{j}=frac{sum_{i=1}^{N}{TWF}_{blue(i,j)}}{{RWR}_{j}}$$
(17)
where (i=1, 2,ldots, N=51;j=1, 2, ldots, J=31,) FSi, CP(i,j) (ton), CD(i,j) (ton), WCj, TWFblue(i,j) (m3), and RWRj (m3) denote the food security for crop i, production of crop i in province j, the demand of crop i in province j, the water crisis in province j, the blue WFs of crop i in province j, and the renewable water resources in province j, respectively.Maximizing the FS index and minimizing the WC index represent the ideal situation. The maximizing function was converted to a minimization function for the purpose of multiobjective optimization. The final form of the objective functions i given by the following equations:$$Min({Z}_{1})=frac{1}{N}{sum }_{i=1}^{N}(1-F{S}_{i})begin{array}{cc},& where,, F{S}_{i}end{array}=Minleft(frac{{sum }_{j=1}^{J}C{P}_{(i,j)}}{{sum }_{j=1}^{J}C{D}_{(i,j)}},1right)$$
(18)
$$Min({Z}_{2})=frac{1}{J}{sum }_{j=1}^{J}W{C}_{j}$$
(19)
where (i=1, 2,ldots , N=51;j=1, 2, ldots, J=31.) The objective function Z1 is calculated based on the food security index expressed as an average for all crops, and the objective function Z2 is calculated as the average of the water crisis indexes in the 31 provinces. Both objective functions are affected by cropping patterns and cultivation areas. The water and land used must be calculated prior to modifying the cropping patterns. The amounts of surplus crops in the provinces and their equivalent water and land are calculated using the following equations:$$ {SCP}_{(i,j)} = Max({CP}_{(i,j)} – {CD}_{(i,j)} + T_{(i,j)} ,0) $$
(20)
$$ {BCY}_{(i,j)} times (X_{1(i,j)} times ICA_{(i,j)} ) + {GCY}_{(i,j)} times (X_{2(i,j)} times RCA_{(i,j)} ) = {SCP}_{(i,j)} $$
(21)
where (i=1, 2,ldots, N;j=1, 2, ldots, J,) SCP(i,j), (X_{1(i,j)}), (X_{2(i,j)}) denote the surplus crop i in province j (ton) determined based on demand and trade in the province, and the percentage of crop i in province j that must be removed from irrigated and rainfed cultivation, respectively. The amount of water and land available for new cultivation are calculated as follows:$$ ICA_{j}^{free} = sumlimits_{i = 1}^{51} {X_{1(i,j)} times ICA_{(i,j)} } $$
(22)
$$ RCA_{j}^{free} = sumlimits_{i = 1}^{51} {X_{2(i,j)} times RCA_{(i,j)} } $$
(23)
$$ TWF_{blue,j}^{free} = sumlimits_{i = 1}^{51} {{WF}_{blue(i,j)} times BCY_{(i,j)} } times ICA_{j}^{free} $$
(24)
where (i=1, 2,ldots , N;j=1, 2, ldots, J,) ICAjfree and RCAjfree denote the total available area of ​​irrigated and rainfed cultivation (ha) in province j, respectively, and TWFblue,jfree represents the total amount of blue WFs available in province j (m3). It is noteworthy that the water and land available in irrigated cultivation can be altered. On the other hand, only the available land is controllable under rainfed cultivation.The objective functions of the proposed method [Eqs. (18) and (19)] were subjected to a set of constraints introduced next.

(i)

Modification of the cropping patterns

The available land in each province is allocated to crops that feature a deficit in the country and are part of the traditional cropping patterns of the provinces. The set of cultivable crops is determined using the following equation:$$ P = left{ {pleft| {p in i,sum_{j = 1}^{31} {SCP_{(p,j)} < 0} } right.} right} $$ (25) where p denotes the set of crops with deficit conditions in the country and SCP(i,j) was defined above. Letting traditional irrigated and rainfed cropping patterns be denoted by Aj and Bj in province j, respectively, the set of irrigated and rainfed crops cultivable in province j was calculated as follows:$$ IC_{j} = P cap A_{j} begin{array}{*{20}c} {} & {(j = 1,2,3,ldots,31)} \ end{array} $$ (26) $$ RC_{j} = P cap B_{j} begin{array}{*{20}c} {} & {(j = 1,2,3,ldots,31)} \ end{array} $$ (27) where (j=1, 2, ldots, J), ICj and RCj denote the irrigated and rainfed crops cultivable in province j, respectively. (ii) Constraint on cultivation area A fraction of ICAjfree can be used in irrigated lands:$$ 0 le M times sumlimits_{i = 1}^{51} {{(X}_{1(i,j)} times ICA_{(i,j)} ) le ICA_{j}^{free} } begin{array}{*{20}c} , & {0 le M le 1} & {} \ end{array} $$ (28) where (j=1, 2, ldots, J), and M denotes the fraction of blue water available. (iii) Constraint on water use The amount of water used to modify the cropping pattern in the provinces is limited:$$ sumlimits_{i = 1}^{51} {TWF_{blue(i,j)}^{m} le RWR_{j} - sumlimits_{i = 1}^{51} {TWF_{blue(i,j)} + } } TWF_{blue,j}^{free} $$ (29) where (left(j=1,2,3,ldots,Jright),) TWFmblue(i,j) denotes the blue WFs used to modify the cultivation in province j, and TWFblue(i,j) represents the initial blue WFs consumed in province j to cultivate crops before changing the cropping pattern.Ideal solution and pareto optimalityThis work applied the multi-objective optimization Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The NSGA is based on the Genetic Evolutionary Algorithm and the Selection, Crossover, and Mutation operations50. The NSGA was introduced by Deb et al.51,Srinivas and Deb52, then improved to the NSGA-II51. The NSGA-II has been widely studied in water resources management53,54,55.The NSGA-II produces a Pareto front of solutions, in which, each point represents a management scenario. The decision-maker selects a scenario based on the objective functions and situational analysis. Multi-criteria decision-making methods (MCDM) can be applied to select an efficient point on the Pareto front curve56,57. This work implements the technique for order preference by similarity to ideal solution (TOPSIS) as the MCDM employed for that purpose. A description of the TOPSIS method is presented in the appendix.The NSGA-II parameters were determined based on a trial-and-error process. Multiple runs of the algorithm were used to adjust the parameters to reduce uncertainty. For this purpose, the population size and maximum iteration were set equal to 400 and 500, respectively, and the crossover and mutation rates were set equal to 0.8 and 0.1, respectively. The flowchart of the proposed approach is displayed in Fig. 1.Figure 1Flowchart of the methodology.Full size image More

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