Markus Andrews

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Shumilova, O. et al. Impact of the Russia–Ukraine armed conflict on water resources and water infrastructure. Nat. Sustain. https://doi.org/10.1038/s41893-023-01068-x (2023). More

Our results show that the most-affected types of infrastructure during the first three months of the armed conflict were dams and reservoirs, underground mines, urban water supply and wastewater treatment systems (overview of this infrastructure in Supplementary Information 2).Ukraine’s critical water infrastructure at riskOf special concern are large reservoirs along the Dnieper River, which are critical for energy production, cooling of nuclear power plants, sustaining agriculture and seasonal flow regulation. In addition, there is a high concentration of settlements along the Dnieper River, with flooding being an immediate threat if the dams would breach (Fig. 3a,b). During World War II, intentional damage to the 800-m-wide dam of the Dnieper HES holding water in the Dnieper Reservoir, near the city of Zaporizhzhia, affected 20,000–100,000 civilians and retreating soviet soldiers crossing the river17 (Fig. 3a). Details on a quantitative flooding-risk assessment for the cascade of Dnieper reservoirs, including those based on hydrological conditions observed in 2022, are presented in Supplementary Information 2.Fig. 3: Examples of impacts on water resources and infrastructure in Ukraine during armed conflicts.a, The dam on the Dnieper River near the city of Zaporizhzhia after reportedly being blown up by Soviet special forces in 1941 in an attempt to delay the offence of German troops. b, Demolition of the dam on the Irpin River on 26 February 2022 caused flooding near the village of Demidov in the Vyshhorod district of Kyiv region. c, Craters formed by shells on the floodplain of the Irpin River. d, Water in the Kamyshevakha River polluted by mine waters (picture taken in 2021). e, Damaged pipe near Kiselevka village in the Kherson region (picture taken in April 2022). f, People in a line for drinking water in Mykolayiv (picture taken in April 2022). Panels adapted with permission from: a, ref. 54, Taras Shevchenko National University of Kyiv; d, ref. 55, Deutsche Welle; e, ref. 56, Korabelov.info; f, ref. 57, Novosti-N. Credit: photographs in b,c, Vincent Mundy.Full size imageApart from flooding, breaching of dams along the Dnieper River poses a danger of secondary radioactive pollution due to uncontrolled release of radioactive material accumulated in the sediments and associated with colloidal materials in surface waters after the disaster at the Chernobyl nuclear power plant (NPP) in 198618,19. Following the accident, the reservoirs of the Dnieper Cascade acted as sinks for radiocaesium, with extensive accumulation recorded in the Kyiv Reservoir. As for radiostrontium, about 43% of the dissolved form that entered the Dnieper system from 1987 to 1993 reached the Black Sea20. Zaporizhzhia NPP, the largest NPP in Europe, is located on the shore of the Kakhovka Reservoir, 40 km downstream from the dam of the Dnieper HES. A sudden loss of water needed for the reactor’s active cooling system can lead to a scenario analogous to the accident at the Fukushima Daiichi NPP in Japan in 201121. The Kakhovka Reservoir also serves as a water source for the largest irrigation system in Ukraine and in Europe22 (for details, see Supplementary Information 2). The conflict raises a risk of either intentional or unintentional bombing posing threats to regional agriculture, food production and international food trade.Military actions and severe environmental pollutionAs a result of the armed conflict, multiple Ukrainian communities have been left without wastewater treatment, resulting in pollution of surface waters. For example, remote-sensing images showed that polluted wastewater was released into the Kakhovka Reservoir when the wastewater treatment plant near Zaporizhzhia ceased operation23. Rivers and networks of irrigation channels that are natural barriers for movement of troops have also become a burial place for military objects (for example, Figure 3c). The underwater decomposition of ammunition leads to release of heavy metals and toxic explosive compounds, with impacts that may last for decades2. This can be critical in the southern regions of Ukraine where an extensive network of irrigation channels exists. Low quality of irrigation water affects the agricultural cropping and the quality of food production24. In the pre-conflict period, the concentrations of heavy metals in waters of the Kakhovka Canal were in compliance with water-quality standards25, but there is concern that the conflict will lead to a deterioration of water quality.In June–July 2022, for the first time, traces of oil products were reported within the area of the surface drinking water intake in the basin of the Siverkyi Donets River, together with exceeded concentrations of mercury, ammonium nitrogen, nitrites, polyaromatic carbons, heavy metals and the insecticide cypermethrin in some rivers within the basin26 (for details on the state of the Siverskyi Donets River since 2014, see Supplementary Information 3). In addition, multiple electrical blackouts within Donbass region have increased the threat of pollution of water sources with mine waters because of failures in operation of pumping equipment. Overflooding of geologically connected mines, a problem present in the region for a long time (for details, see Supplementary Information 2), leads to increase in the concentration of salts in mine water up to 20–70% (except for chloride) and can double concentrations of organic substances and hydrocarbons27. High concentrations of sulfates, chlorides and heavy metals in mine waters pose severe risks for groundwater and surface-water quality (for example, the Kamyshevakha River has become severely polluted by mine waters since 2018; Fig. 3d).Access to safe water resources and the danger of epidemicsDuring the armed conflict, water supply infrastructure has been subjected to repeated attacks, with limited time and few opportunities for repair and recovery. By 20 April 2022, the United Nations reported that 6 million people in Ukraine were struggling every day to get access to drinking water, with 1.4 million people being reported to lack access to safe water in the east of the country and another 4.6 million people having only limited access28. For the period between March and December 2022, the UN estimates that some 16 million people in Ukraine will need water, sanitation and hygiene assistance29. In the city of Mariupol, more than 40% of the water supply system is reportedly damaged, and on 17 May 2022, the World Health Organization raised concerns about the danger of a cholera epidemic in the city due to mixing of sewage and drinking water30. In Mykolayiv, the population was left without a centralized water supply for more than a month (Fig. 3e,f), and water supplied with interruptions from an alternative source later had excessive concentrations of chlorides, sulfates and other mineral salts even after treatment31. The population of Donetsk is reportedly receiving water for only two hours once every 3–4 days, and all specialists capable of addressing problems with the water system are mobilized in the armed conflict, limiting the ability to repair the system32. The Luhansk region, with a pre-conflict population of 2.1 million, was left completely without water supply in the beginning of May, and delivery of water was possible only externally through humanitarian organizations. The lack of access to clean water poses a serious threat of epidemic outbreaks, which was worsened by both extremely hot temperatures observed during the summer in 2022 and reduced capabilities of the medical system33. According to UNICEF, children living through prolonged conflicts are more likely to die from water-borne diseases than from the military conflict itself34.Caveats and uncertaintiesExpert evaluation of reported and projected impacts of armed conflict is limited in many cases by the lack of safe access to affected sites and by possible biases and discrepancies in reporting. However, to a certain extent, consequences of the use or targeting of water systems in conflicts can be estimated on the basis of retrospective analyses of similar impacts on freshwater resources and infrastructure. For example, catastrophic flooding due to damage to the Dnieper HES during World War II and the spread of radionuclides through water as a result of the catastrophe at Chernobyl NPP indicate the spatial extent of potential impacts in cases when large reservoirs or NPPs are affected by military actions. The long-lasting consequences of environmental pollution due to impacts on water infrastructure have been highlighted by an accident of a potash spill into the Dniester River due to overflooding of the Stebnik waste pond in the Lviv region in 198335,36. In this event, more than 3.8 km3 of highly concentrated waste salts were spilled, raising the salinity of the Dniester River to levels higher than seawater. This event disrupted water supply to millions of people in Odessa, Kishinev and the Tiraspol region, killed hundreds of tons of fish and heavily contaminated the sediments of the river35,37.Although modern military technologies can allow precise destruction of localized objects, the damage to industrial targets is not always environmentally local, and many of the attacks have been not precise but general. In highly industrialized Ukraine38, targeting urban and industrial infrastructure leads inevitably to widespread and severe environmental consequences. By the beginning of June 2022, more than 25 big Ukrainian industrial companies were damaged or fully destroyed. Most prominent are the ammonia producer AZOT, the Coke and Chemistry concern in Avdievka and the centre of metallurgy AZOVSTAL in Mariupol39. Port infrastructures in the Black Sea and Azov Sea coastal areas were heavily bombed in Mykolayiv, Odessa and Mariupol.Other impacts on water resources can be only roughly estimated at the moment, including the threat to regional biodiversity. It has been reported that 14 Ramsar wetland sites covering 400,000 hectares along the coastline and lower reaches of the Dnieper River are under threat40. Damage to reservoirs during spring spawning led to mass fish deaths (confirmed for the Oskil Reservoir)41.The need for urgent actionOur study on the impacts of the armed conflict on freshwater resources and water infrastructure in Ukraine highlights diverse and long-lasting consequences not only for local populations and ecosystems, but also for progress towards the global Sustainable Development Goals42.Catchments cut across political borders and pollutants released into the environment from armed conflicts can spread across national borders. Ninety-eight percent of the catchment area of Ukrainian rivers flows to the Black Sea and Azov Sea, and the remaining 2% to the Baltic Sea. Although the international community has already identified the risk of environmental pollution in the Donbass region in the eastern part of Ukraine since 201443, military actions have dramatically intensified and are now taking place in the previously unaffected southern part of Ukraine. This area is important for agricultural activities that depend on an extensive network of irrigation channels. According to the World Food Programme, Ukraine contributed 50% of sunflower oil and 10% of wheat to the total global exports in 2021, being the first and the sixth global producer, respectively44. Due to the armed conflict, agricultural production has been substantially reduced, leading to food shortage on the global scale, with countries of Middle East and Africa most affected11.A lack of access to safe water and the environmental threats urge prompt action. Priority activities should focus on providing safe drinking water for millions of civilians in the affected areas and protecting civilian water supply and treatment systems. A set of international rules related to protection of the environment and civilian water infrastructure during armed conflicts is defined by the Geneva List of Principles, including especially the 1977 Protocols to the Geneva Convention4,45. According to the recent resolution adopted by the United Nations Security Council on 27 April 2021, all parties of the armed conflict are obliged to protect civilians and civilian infrastructure, including water facilities46. Nevertheless, multiple cases of attacks on water technicians since the start of the conflict have been reported in Chernihiv, Kharkiv and Mykolayiv, adding to at least 35 water engineers who have been killed or injured in the Donetsk and Luhansk region since 201447,48. We argue that protection of civilian water technicians should be ensured, providing the so-called ‘green corridors’ for safe access to water infrastructure.Support by international agencies and partners is needed to provide water-treatment systems that can be used by individual households and to provide temporary access to safe drinking water or assistance in rebuilding and replacing destroyed civilian water infrastructure. For places without current access to safe drinking water, sustainable options should be investigated apart from the temporary and costly option of transporting bottled water. In particular, water-treatment systems should be installed at critical locations such as hospitals, schools and community centres. Individual households could be supplied with individual small-scale filtration systems. In the longer term, options such as desalination should be considered because most of the local surface waters in the southeastern parts of the country are characterized by high mineralization49 (for example, the current water supply to Mykolayiv from the Southern Bug to replace the damaged supply system from the Dnieper30). For settlements that were receiving water from the basin of the Siverskyi Donets River, the option for desalination is even more convincing due to both the proximity to alternative water supply sources and the fragility of water-transfer facilities as has been shown by this armed conflict.Importantly, environmental monitoring and data collection efforts to better understand the environmental risks are urgently needed. Unfortunately, in March 2022, the Organization for Security and Cooperation in Europe, the official international conflict monitor, announced closure of its Special Monitoring Mission in Ukraine50. The mission was enabling the repair and maintenance of the critical civilian infrastructure facilities benefitting civilians on both sides of the contact line in eastern Ukraine since 2014.The current crisis demands coordinated action from Ukraine and Russia, mediated and facilitated by other countries of the European Union and the United Nations. We recommend that science and management focus on assessing the dynamic state of the environment and water conditions in the zone of the conflict, with the aim to develop effective and prompt approaches for its post-war rebuilding. Although the conflict is still ongoing, freshwater resources and water infrastructure should be protected and maintained because of their central role in supporting basic human needs, health and well-being. Because access to the sites in the zone of conflict is limited, particular attention should be given to spatial mathematical and cartographic modelling using remote-sensing data, which allow efficient use of limited input information. Such an approach can be applied to simulating flooding due to dam breaching under different hydrological scenarios, spread of pollutants from sunken military monitions, effect of land mines on surface and groundwater, predicting quality of subsurface mine waters and their overflow to geologically connected areas, forecast of quantity and quality of water for drinking and irrigation purposes and assessment of the effect on freshwater biodiversity. From a management perspective, we recommend that future studies focus on assessing financial apparatus and the economic dimensions of sustainable water management, on the enforcement of water-related regulations and on identification and evaluation of current and post-conflict needs, facilitating the recovery of Ukrainian water resources and infrastructure. More

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PVWPS operationThe motor and the pump are built in together14 and the motor-pump set is submersed in the borehole under the water43. Control equipment is also installed between the PV modules and the motor-pump and/or integrated to the motor-pump set in the borehole14,17. This equipment allows the motor-pump to stop and also to operate the motor-pump and the PV modules at their best operating points14. Once the water is pumped, it might then be stored in a water tank to mitigate the variability of solar resources14,29. When pumping starts, a cone of depression of radius rc is formed and there is a drawdown Hb,d in the borehole (see Fig. 1). The higher the pumped flow rate, the higher the drawdown Hb,d and therefore the deeper the water in the borehole Hb. If Hb reaches the position of the motor-pump Hmp, the motor-pump automatically switches off, therefore preventing the motor-pump from running dry44. The motor-pump remains shut down during a period Δtshut, after which it makes an attempt to restart44.Input data processingWe observe in Table 1 that the datasets have varying spatial resolutions. In the article, we use the spatial resolution of the irradiance map, 0.2° (~22 km). Indeed, this resolution is sufficient for the purposes of this article and it allows to divide computing time and memory requirements by ~16 in comparison to the 0.05° resolution. At this 0.2° resolution, the total area of Africa of 30 million km2 is divided into 62,000 pixels. We apply this resolution of 0.2° to all datasets by nearest interpolation.No exact value of the static water depth Hb,s, transmissivity T and saturated thickness Hst are provided for each location by the original source but only a range of variation. For instance, for −15.8° (lat) & 21.9° (lon), the saturated thickness Hst is comprised between 25 and 100 m. In most cases, we consider the middle of the range (e.g., 62.5 m in the example). The only two exceptions are: when Hb,s is higher than 250 m, we consider 300 m (same for Hst); and, when Hb,s is between 0 and 7 m, we consider 7 m45. Due to the lack of available information, the input groundwater data provided in Table 1 are considered to remain constant over time.Reference46 provides complete irradiance data with a time step of 15 min from 2013 to 2020 across Africa. In this article, except mentioned otherwise, we use irradiance data from 2020 with a 30-min time step (by taking one point every two 15-min points), instead of all the available complete irradiance data. It divides computing time and memory requirements by ~16. Additionally, it produces reduced and acceptable deviations on the results. Indeed, for 100 randomly chosen locations, we simulated the pumped volume V, for the three considered PVWPS sizes, using (1) irradiance data from 2013 to 2020 with a 15-min time step and (2) irradiance data from 2020 with a 30-min time step. For these locations, the absolute error on volume V is systematically lower than 7.9% and the average absolute error is 2%. These results are coherent with the observed low influence of irradiance on the pumped volume in comparison to groundwater resources. Thanks to the consideration of this reduced irradiance vector, the random access memory (RAM) and the computing time required to obtain a map of final results (such as Fig. 5b) are respectively 38 Gb and 10 h (time for Intel Xeon E5-2643 3.3 GHz processors and 96 GB RAM, running on Debian 4.19.194-2), which is more reasonable.Atmospheric sub-modelFor each location, the irradiance on the plane of the PV modules Gpv at time t can be deduced from satellite data by47,48:$${G}_{{{{{{rm{pv}}}}}}}left(tright)={G}_{{{{{{rm{bn}}}}}}}left(tright){{cos }}left({{{{{rm{AOI}}}}}}left(t,theta ,alpha right)right)+{G}_{{{{{{rm{gh}}}}}}}left(tright)kappa frac{1-{{cos }}left(theta right)}{2}+{G}_{{{{{{rm{dh}}}}}}}left(tright)frac{1+{{cos }}left(theta right)}{2}$$
(1)
where κ is the albedo of the surrounding environment, θ and α are the tilt and azimuth of the PV modules and AOI is the angle of incidence between the sun’s rays and the PV modules. The albedo κ is taken equal to 0.2 because it corresponds to the albedo of cropland, which is a common environment in the rural areas considered49. In any case, additional simulations show that the value of the albedo has a negligible effect on the pumped volume V. AOI is computed using the MATLAB toolbox PVLIB developed by the Sandia National Laboratories50.For each location, the azimuth α and the tilt θ of the PV modules are chosen to maximize the irradiance on the plane of the PV modules Gpv. The azimuth α is taken equal to51:$$alpha =left{begin{array}{c}180^circ quad {{{{{rm{if}}}}}},phi , > ,0\ 0^circ quad {{{{{rm{if}}}}}},phi , < ,0end{array}right.$$ (2) where ϕ is the latitude of the location. The tilt is taken equal to51:$$theta =left{begin{array}{c}{{max }},(10,1.3793+(1.2011+(-0.014404+0.000080509phi )phi )phi )quad{{{{{rm{if}}}}}},phi > ,0\ {{min }},(-10,-0.41657+(1.4216+(0.024051+0.00021828phi )phi )phi )quad{{{{{rm{if}}}}}},phi , < ,0end{array}right.$$ (3) As evidenced by Eq. (3), the tilt should be higher than 10° or lower than −10°, so that the PV modules are tilted enough to be cleaned when it rains.Photovoltaic modules sub-modelConsidering that the maximum power point tracking of the PV modules is correctly performed, a simplified model to compute the power P produced by the modules is used:$$Pleft(tright)=frac{{G}_{{{{{{rm{pv}}}}}}}left(tright)}{{G}_{0}}{P}_{{{{{{rm{p}}}}}}}left(1-{c}_{{{{{{rm{pv}}}}}},{{{{{rm{loss}}}}}}}right)$$ (4) where G0 is the reference irradiance (1000 W m−2), Pp is the peak power of the PV modules in standard test conditions (STC) and cpv,loss is a coefficient that represents the losses (e.g., soiling, temperature, mismatch, wiring52,53) at the level of the PV modules. For the sake of simplicity, and as we consider a generic PVWPS, we consider that cpv,loss is independent of the operating point of the PV modules, of the time, and of the location. We take it constant, equal to a single value (see Table 2).Hydraulic sub-modelThe total dynamic head TDH between the motor-pump and the pipe output is given by54:$${TD}Hleft(tright)={H}_{{{{{{rm{b}}}}}}}(t)+{H}_{{{{{{rm{p}}}}}}}(t)$$ (5) where Hb is the water depth in the borehole and Hp is the additional head due to pressure losses in the pipe.The water depth in the borehole Hb is given by (see Fig. 1)42:$${H}_{{{{{{rm{b}}}}}}}(t)={H}_{{{{{{rm{b}}}}}},{{{{{rm{s}}}}}}}+{H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}(t)$$ (6) where Hb,s is the static water depth and Hb,d is the drawdown. The drawdown is composed of two parts:$${H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}(t)={H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{a}}}}}}}(t)+{H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{b}}}}}}}(t)$$ (7) where ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{a}}}}}}}(t)) is the head loss due to aquifer losses and ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{b}}}}}}}(t)) is the head loss due to borehole losses.The head loss due to aquifer losses ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{a}}}}}}}(t)) depends on the pumping flow rate Q, the aquifer transmissivity T, the borehole radius rb, and a length parameter rc representing the distance of water travel to replace the water pumped out. From dimensional analysis, we expect that (tfrac{{H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{a}}}}}}}left(tright)cdot T}{Qleft(tright)}) should be a function of (tfrac{{r}_{{{{{{rm{c}}}}}}}}{{r}_{{{{{{rm{b}}}}}}}}). We thus propose the following model for ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{a}}}}}}}), which is derived from Thiem equation55:$${H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{a}}}}}}}left(tright)=frac{{{{{{rm{ln}}}}}}left(frac{{r}_{{{{{{rm{c}}}}}}}}{{r}_{{{{{{rm{b}}}}}}}}right)}{2pi T}Qleft(tright)$$ (8) where rc can be considered the effective radius of the cone of depression. This model satisfies horizontal, radial and steady Darcy flow in a uniform, homogeneous and isotropic aquifer. It captures the essential features for aquifer losses: ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{a}}}}}}}(t)) proportional to pumped flow rate and inversely proportional to transmissivity45. Though the flow is transient, only simplified steady-state models, as the one of Eq. (8), can be applied with the available information as dynamic models would require pumping tests. Furthermore, we consider that the radius of the cone of depression rc is comprised between 100 and 1000 m and, to correlate it to a measured quantity, that it depends linearly on the groundwater recharge R: for the lowest recharge (0 m/year), rc is equal to 1000 m; for the highest one (0.2947 m year−1), rc is equal to 100 m; in-between, rc is obtained linearly from the recharge (rc = 1000–3054 · R). Thus, groundwater recharge R is used to constrain the size of the cone of depression.The head loss due to borehole losses ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{b}}}}}}}(t)) is given by56:$${H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{b}}}}}}}left(tright)=beta {Q}{left(tright)}^{2}$$ (9) where β is a coefficient related to the borehole design. For the yields considered in this article, ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{b}}}}}}}(t)) usually remains lower than a few meters but, as ({H}_{{{{{{rm{b}}}}}},{{{{{rm{d}}}}}}}^{{{{{{rm{b}}}}}}}(t)) depends on the square of the pumped flow rate, it may be more important for larger abstraction capacities.The additional head due to pipe losses Hp is given by57:$${H}_{{{{{{rm{p}}}}}}}left(tright)={H}_{{{{{{rm{p}}}}}},{{{{{rm{ma}}}}}}}left(tright)+{H}_{{{{{{rm{p}}}}}},{{{{{rm{mi}}}}}}}left(tright)$$ (10) where Hp,ma(t) corresponds to losses that occur along the pipe length (also called “major losses”) and Hp,mi(t) corresponds to losses at junctions such as elbows and curvatures (also called “minor losses”). Hp,ma(t) is given by57:$${H}_{{{{{{rm{p}}}}}},{{{{{rm{ma}}}}}}}left(tright)=frac{8f}{{pi }^{2}g{D}_{{{{{{rm{p}}}}}}}^{5}}{L}_{{{{{{rm{p}}}}}}}Q{left(tright)}^{2}$$ (11) where g is the gravitational acceleration (9.81 m s−2), Dp is the pipe diameter, Lp is the pipe length, Q is the pumped flow rate, and f is the friction coefficient between the water and the pipe. We approximate the pipe length Lp to be equal to the depth of the motor-pump Hmp (see Fig. 1). The expression of f depends on the value of the Reynolds number ({{{{{rm{Re}}}}}}=tfrac{4Q}{pi {D}_{{{{{{rm{p}}}}}}}w}), where w is the water kinematic viscosity (taken equal to 1 × 10−6 m2 s−1)57: for Re More

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Study design and hypothesesThe encouragement of the Chinese central government to local governments to adopt the PPP model through the top-down procedure and build and operate WTI has created favourable external policy circumstances for the development of wastewater treatment PPP projects. However, the acceptance of the PPP model by both local governments and private capital is rooted in the positive effect of it on improving the UWTE. In China there is no completely private-owned WTI before. Compared to the original government monopoly on the construction and operation of the WTI, the introduction of private capital participation is equipped with conditions to improve the UWTE. The participation of private capital can donate sufficient funds, scientific management experience, and advanced technology to the construction and operation of the regional, quasi-natural monopoly, and public welfare WTI21, which are key elements that determine the UWTE. Furthermore, the urban wastewater treatment field was in a state of no market competition before the introduction of private capital, and the government’s early monopoly ensured that private capital could obtain both economic benefits and performance with exclusive agency rights after joining. Meanwhile, the government would conduct a performance assessment of the quality of wastewater treatment during construction and operation, and private capitals whose wastewater treatment efficiency failed to meet the requirements would be barred from obtaining performance benefits40. Therefore, private capital is inherently incentivised to ensure the UWTE and minimise profit loss. Most of the private capital involved in the construction and operation of WTI in China comes from state-owned enterprises, partly due to the remarkable cooperation between the local government and state-owned enterprises at the beginning of the market economy reform41. This is convenient for both sides in reducing the cost of supervising due to information asymmetry in the principal-agent relationship and to facilitate the unique advantages of state-owned capital to undertake social responsibility. Therefore, Hypothesis 1 is proposed:UWTE is high in prefecture-level cities that have introduced the PPP model compared to prefecture-level cities that have not adopted the PPP model for the construction and operation of WTI.The return mechanism is related to the risk sharing of the costs of WTI construction and operation. Part of the purpose of introducing private capital is to share the cost risk of the government’s monopoly on the construction and operation of infrastructure by exchanging the government’s appropriate concession of operating revenue42; however, excessive cost and risk sharing reduces the probability of private capital participation in the construction and operation of infrastructure43. In China, the return mechanisms of the public–private WTI include user payment, government payment, and feasibility gap subsidy, of which the cost risks of construction and operation under the first two return mechanisms are primarily borne unilaterally by the private capital and the government, respectively44, whereas the cost risks of construction and operation under the latter are borne by the government to fill the gap of user payment39. Accordingly, Hypothesis 2 is proposed:The return mechanism of the feasibility gap subsidy has a greater impact on improving the UWTE than the mechanisms of user payment and government payment.The way to choose private capital to cooperate with the government is related to the efficiency of the construction and operation of the WTI. Private capital selected through competitive procurement usually exhibits sufficient funds, scientific management experience, and innovative technology45. Cooperation between the government and this type of private partner helps obtain the optimal construction and operation plan at the lowest cost. The adoption of competitive procurement can improve efficiency while saving transaction costs, especially for infrastructures with large capital scale and long term and complicated operational systems, such as urban wastewater treatment38. In China, the competitive procurement mechanism of PPPs for WTI includes public bidding, competitive negotiation, invitational bidding, and competitive consultation, whereas the non-competitive procurement mechanism mainly refers to single-source procurement46. In accordance with this, Hypothesis 3 is proposed:The competitive procurement mechanism has a greater impact on improving the UWTE than the single-source procurement mechanism.The PPP is ultimately a contract between the principal and the agent that specifies how risks are shared and how benefits are distributed40. Construction and operation of WTI under the PPP model usually require long-term contracts. This means that contracts are often incomplete, and the allocation of remaining control rights has a significant impact on the incentives for private capital parties to participate. Existing research suggests that the greater the remaining control the private capital receives, the stronger their incentive to participate in the construction and operation of infrastructure, and the more they pursue innovation and efficiency47. The remaining control right is related to the manner in which the infrastructure is operated48. In China, PPPs for WTI operate through outsourcing (e.g. Operation and Maintenance [OM], Management Contract [MC], and Build-Transfer [BT]), franchising (e.g. Build-Operate-Transfer [BOT], Build-Own-Operate-Transfer [BOOT], Transfer-Operate-Transfer [TOT], and Rehabilitate-Operate-Transfer [ROT]), and privatisation (e.g. Build-Own-Operate [BOO] and Buy-Build-Operate [BBO]). Therefore, Hypothesis 4 is proposed:Privatised operations have a greater impact on improving UWTE than outsourcing and franchising.Promotion after demonstration has long been a feature of public policy formulation and implementation by the Chinese government, and this is also true for the construction and operation of wastewater treatment PPP projects. Selecting a portion of these projects for demonstration can facilitate pre-judgement of the issues encountered in the construction and operation of infrastructure and improve efficiency49. The demonstration of WTI is prioritised for various government policies and funding support and is subject to stringent monitoring by the government50. Therefore, to obtain priority support from the government, WTIs that have not entered the demonstration have greater motivation to perform higher quality wastewater treatment. In this case, Hypothesis 5 is proposed:Wastewater treatment PPP projects that have not yet entered the demonstration have a UWTE higher than those that have been in the demonstration.Quantifying the UWTE using DEAIn order to measure the efficiency represented by the capacity to increase output at a given input, two methods have been proposed. One is the estimation method based on parameters. The common method is stochastic frontier analysis (SFA). The other is based on the nonparametric estimation method, and the DEA is the most widely used. Although SFA can consider the influence of random factors on output, it needs to determine the specific form of production frontier as the condition when measuring efficiency. This means that if the pre-set production function form is inconsistent with the reality, the efficiency of the measure is not accurate. In contrast, the advantage of DEA is that there is no need to presuppose a specific production function form. It is based on a number of input and output indicators, using the method of linear programming, with the data envelope frontier as the comparison base, the decision making unit (DMU) of the same type of relative evaluation to determine the efficiency. In addition, DEA can also give the improvement space of each DMU in terms of input and output, which is convenient to give optimisation suggestions. Thus, DEA is widely used to assess the efficiency of public services, the environment, and natural resources fields51. With different settings of comparative DMUs, DEA can be divided into the CCR model, which assumes that the comparative DMUs meet the condition of constant returns to scale, and the BCC model, which assumes that the comparative DMUs meet the condition of variable returns to scale, and Shephard distance function introduced to distinguish pure technical efficiency from scale efficiency and determine whether the DMU production is optimal. Most studies have concluded that the BCC model is more consistent with the reality of production52; therefore, it is widely accepted and adopted compared to the CCR model. In this study, DEA based on the BCC model was used to measure the UWTE. The length of the urban wastewater network and the daily treatment capacity of urban wastewater treatment plants are established as input indicators, and the total amount of urban wastewater treatment is established as the output indicator53. The efficiency for each DMU is measured by solving the following linear programming of the BCC model, shown in Eq. (1):$$begin{array}{l}max theta \ s.t.mathop {sum }limits_{i = 1}^{283} lambda _i cdot lwn_i le lwn_{i_0}\ mathop {sum }limits_{i = 1}^{283} lambda _i cdot dtc_i le dtc_{i_0}\ mathop {sum }limits_{i = 1}^{283} lambda _i cdot tawt_i le theta tawt_{i_0}\ lambda _i ge 0\ mathop {sum }limits_{i = 1}^{283} lambda _i = 1end{array}$$
(1)
where subscript θ denotes the evaluated DMU. lwni and dtci represent the inputs, i.e. length of the wastewater network and the daily treatment capacity of urban wastewater treatment plants in prefecture-level city i, respectively, and the output is tawti, the total amount of wastewater treatment of each prefecture-level city. is a λ vector of intensity variable, and θ represents the efficiency score based on the input-output calculation. This is the UWTE to be calculated in this study.Causal linking the PPPs to the UWTE using DEA-Tobit regression modelThe DEA-Tobit regression model was used to empirically test the causal relationship between the PPPs and the UWTE. It is meaningful to use DEA to measure the UWTE, because the measured relative efficiency can be used to evaluate the capacity of urban wastewater treatment, and make it possible to compare the capacity of urban wastewater treatment between prefecture-level cities, and also creates conditions for finding the factors affecting the UWTE. As the range of UWTE measured by DEA is between 0 and 1, it does not obey the normal distribution and violates the classical assumption of ordinary least squares estimation. Therefore, in order to avoid the bias caused by OLS estimation, the restricted dependent variable model, also known as the Tobit regression model, is usually adopted in previous studies. The regression model which combines DEA and the Tobit regression model is also called the DEA-Tobit regression model. This study employs a DEA-Tobit regression model based on panel data, shown in Eq. (2).$$uwte_{it} = beta _0 + beta _1 cdot PPP_{it} + X^prime cdot gamma + varepsilon _{it}$$
(2)
where uwte denotes the efficiency of urban wastewater treatment. PPP denotes the degree of development of urban wastewater treatment PPP projects, which is measured in three calibres by determining the presence or absence of wastewater treatment PPP projects, the number of wastewater treatment PPP projects, and the investment amount of wastewater treatment PPP projects. X′ denotes other main control variables that potentially affect UWTE including population density, urbanisation rate, GDP per capita, industrialisation rate, openness, and green innovations. i and t represent prefecture-level city and year, respectively. β0 and εit denote the intercept term and the random disturbance term, respectively. β1 and γ are both parameters to be estimated, and β1 is significantly positive, indicating that the PPP model has a significant positive effect on the UWTE. Because the DEA-Tobit regression model with panel data does not have consistent and unbiased parameter estimates obtained under the fixed effects, the random effects estimation method is used in this study, referring to the parameter estimation recommendations presented by Liu et al.54Measurements of dependent, explanatory and control variablesThe dependent variable in this study is the UWTE. As mentioned above, we use DEA based on the BCC model to measure the UWTE. The closer the value of UWTE is to 1, the higher the efficiency is; the closer it is to 0, the lower the efficiency is.The degree of PPP development is the key explanatory variable of this study. It can be measured in various ways. The most common approach is determining the presence or absence of PPP projects, the number of PPP projects, and the investment amount of PPP projects31,33. To assess the impact of PPP on the UWTE in a comprehensive and reliable manner, this study uses all three metrics simultaneously.The endogeneity of mutual causation must be addressed when investigating the causal relationship between PPPs and the UWTE. This is because prefecture-level cities that use PPP models to build and operate WTI may consider wastewater treatment to be important, for example, the promotion of local government officials is closely related to the quality of public services in their jurisdictions during their tenure. To obtain a higher promotion probability, these prefecture-level cities focus on the efficiency of urban public services, including wastewater treatment, and the higher UWTE determines their willingness to adopt PPPs. Therefore, this study uses instrumental variables to eliminate the endogeneity problem in the regression analysis.Exogenous and correlation conditions are required for suitable instrumental variables. Waste treatment PPP development measured by determining the presence or absence and the number of waste treatment PPP projects is an instrumental variable for the degree of wastewater treatment PPP projects. This is because waste treatment and wastewater treatment are both urban environmental protection infrastructures. Furthermore, prefecture-level cities that consider wastewater treatment are highly likely to consider waste treatment, which are highly correlated. The PPP development for waste treatment does not directly affect the UWTE. Furthermore, the mean number of wastewater PPP projects in neighbouring prefecture-level cities in the prefecture-level city’s province was an instrumental variable for wastewater treatment PPP projects there. This is because, on the one hand, local government officials proactively follow the practices of other neighbouring prefecture-level cities in the province55. Assuming that other neighbouring prefecture-level cities in the province are inclined to promote wastewater treatment PPP projects, the prefecture-level city is highly likely to adopt a PPP model for the construction and operation of WTI. However, the mean number of wastewater treatment PPP projects in other neighbouring prefecture-level cities in the province will not directly affect UWTE in the prefecture-level city.Control variables: based on IPAT theory56, population density, urbanisation rate, GDP per capita, industrialisation rate, openness, and green innovations were selected in this study to measure the influence of three dimensions of population, wealth, and technology on the UWTE. The population density is measured as the urban population divided by the urban area. The higher the population density, the greater the need for an urban wastewater treatment capacity. The urbanisation rate is calculated as the share of urban population in the total population of the prefecture-level city. The higher the urbanisation rate, the higher the population in urban areas and the higher the demand for urban wastewater treatment capacity. Meanwhile, the urban population produces relatively more wastewater.GDP per capita is measured as GDP divided by population. The higher the GDP per capita, the higher the level of economic development of the prefecture-level city, and the more the government can regulate urban wastewater35, thus affecting the UWTE. The industrialisation rate is obtained by calculating the ratio of the output value of the secondary industry to GDP. The higher the industrialisation rate, the greater the demand for urban water resources, and more wastewater discharges are generated2, which affects the UWTE. Openness is measured by the proportion of imports and exports to GDP. The higher the openness, the more likely it is to attract companies with advanced environmental technologies57, reducing the amount of wastewater discharged from the prefecture-level city’s production sector. The ‘pollution heaven’ hypothesis may attract additional pollution discharge enterprises to the prefecture-level city58, affecting the prefecture-level city’s UWTE. Green innovations are measured using the number of green patents for wastewater treatment. Green patents for wastewater treatment are obtained from the Green List of International Patent Classification provided by the World Intellectual Property Organization (WIPO). If there are green patents for wastewater treatment, the reduction of wastewater discharge from enterprises is more likely, and thus the UWTE is improved59. This study considers the logarithm of the number of green patents for wastewater treatment to avoid the influence of data heteroscedasticity on the regression estimation results.DataThe research sample in this study comprised 1303 wastewater treatment PPP projects in 283 prefecture-level cities in China from 2014 to 2019, excluding Hong Kong, Macao, and Taiwan. To estimate the impact of PPPs on the UWTE, we needed data on the length of urban wastewater network, daily treatment capacity of urban wastewater treatment plants, total amount of urban wastewater treatment, wastewater treatment PPP projects, population density, urbanisation rate, GDP per capita, industrialisation rate, openness, and green innovations. Data on the length of the urban wastewater network, the daily treatment capacity of urban wastewater treatment plants, and the total amount of urban wastewater treatment were obtained from the China Urban Construction Statistical Yearbook 2014–201960. The PPP data were obtained from the Ministry of Finance’s Public–Private Partnerships Center61 and were captured by python technology. Data on population density, urbanisation rate, GDP per capita, industrialisation rate, and openness were obtained from China City Statistical Yearbook 2015–202062, and data on green patents were obtained from China National Intellectual Property Administration63. Supplementary Table 1 presents the descriptive statistics of the main variables, and Supplementary Fig. 1 reports the UWTE of 283 prefecture-level cities in China from 2014 to 2019. More

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