How Battery Makers in China Could Reshape the Science of Pollution Permits

In the race to electrify transport and store renewable energy, batteries have become the defining technology of the decade. Yet the factories that produce them consume water and release wastewater just like any heavy industry, even though they rarely appear on official lists of high-pollution sectors. A new study published in Water Resources Management tackles this blind spot head-on, asking a deceptively simple question: when a region’s water can only absorb so much pollution, how should a government decide which battery enterprises get to discharge, and how much? The answer, developed by researchers led by Haijiao Du and Wei Wan of Nanchang University using more than a decade of data from Changxing County in eastern China, may influence how pollution permits are handed out far beyond one county.

The research focuses on a concept that sounds technical but has enormous practical consequences: the allocation of discharge rights. In China and many other countries, regulators cap the total amount of wastewater and pollutants that may enter a river system, then divide that cap among individual enterprises as permits. How that division is performed determines which factories grow, which shrink, and whether a river stays clean. For industries with obvious pollution footprints, such as papermaking or electroplating, allocation rules are relatively well established. But battery energy enterprises occupy an awkward middle ground: their water use and pollutant loads are significant, yet they are not classified among the explicitly high-environmental-cost industries, so they often escape rigorous permit allocation.

Changxing County, in Zhejiang Province near Lake Taihu, offered an ideal testing ground. The county hosts a cluster of battery manufacturers and sits within a dense river network where water quality is a persistent concern. The team assembled data on water pollutant discharge from battery energy enterprises between 2010 and 2023, covering conventional indicators such as chemical oxygen demand, a measure of organic pollution, and ammonia nitrogen, a nutrient that can trigger algal blooms and fish kills. Using this record, the researchers first asked how much pressure the county’s water systems could actually withstand.

That question was answered through two complementary assessments. The first is water resources carrying capacity, or WRCC, which describes how much water a region can supply and absorb without degrading the resource base itself. The second is water environmental capacity, or WEC, which quantifies how much pollutant load a water body can assimilate while still meeting quality standards. The two are related but not identical: a region may have plenty of water in quantity terms yet lack the environmental headroom to absorb more contamination, or the reverse. The study evaluated both constraints across seven sub-regions of the county, designated Regions I through VII, to establish the physical ceiling within which any permit allocation must operate.

The spatial analysis produced one of the study’s most striking findings. The two constraints are inconsistent across space in ways that frustrate simple policy. Region III, for example, showed an overloaded water resources carrying capacity, meaning its water quantity was stretched beyond sustainable limits, yet it still retained remaining water environmental capacity, meaning its rivers could accept additional pollutant loads. Region II displayed exactly the opposite pattern: adequate water resources but no environmental room left for more pollution. This mismatch means a single, uniform allocation rule would misfire. A region short on water but rich in assimilative capacity needs different incentives than one flush with water but ecologically saturated. The authors argue this spatial inconsistency demands a zoning-based management scheme that treats WRCC and WEC as dual, simultaneous constraints rather than interchangeable measures.

With the constraints established, the team turned to the core allocation problem. They applied five distinct methods to simulate how discharge rights could be divided among battery enterprises within the key regions. The first, Proportional Allocation, simply distributes permits in proportion to existing discharge levels, a transparent but arguably conservative approach that rewards historical pollution. The second, the Discharge Performance method, allocates based on how efficiently each enterprise uses water and generates pollution per unit of output, favoring cleaner producers. The third, the Information Entropy Weight method, borrows from information theory, using entropy to weight multiple criteria objectively and reduce the arbitrary influence of human judgment. The fourth, Data Envelopment Analysis, is an operations research technique that measures the relative efficiency of each enterprise against a production frontier, allocating more permits to those operating closest to best practice. The fifth is an improved version of the entropy weight method, refined by the authors to correct known weaknesses in the standard formulation.

The results revealed a sobering truth about permit allocation: the method you choose changes everything. When the five approaches were run on the same enterprise data, the quota shares assigned to individual companies varied dramatically from one method to another. An enterprise that would receive a generous allocation under proportional rules might receive a fraction of that under efficiency-based or entropy-weighted schemes. This method-dependence is not a mathematical curiosity; it translates directly into competitive advantage or disadvantage for real firms, and into different total pollution loads for real rivers. Yet in practice, many regulators select an allocation method by habit or convenience, without systematic evaluation of its consequences.

To bring order to this confusion, the researchers built a performance evaluation system with three pillars: efficiency, fairness, and feasibility. Efficiency asks whether permits flow to enterprises that use water and generate pollution most productively. Fairness considers whether the distribution treats comparable enterprises comparably and respects legitimate differences in scale and history. Feasibility asks whether the resulting allocation could realistically be implemented given administrative capacity and enterprise circumstances. Scoring all five methods against these criteria produced a clear ranking: the improved Information Entropy Weight method, designated M5, performed best, followed by the standard entropy method M3, then Data Envelopment Analysis M4, the Discharge Performance method M2, and finally Proportional Allocation M1. The improved entropy method won because it combined high efficiency with moderate fairness and feasibility, a balance the other approaches failed to strike. Purely proportional allocation, while easy to defend politically, scored worst because it entrenches historical inefficiency.

From these findings the authors distill three policy recommendations. First, regions should establish a joint WRCC-WEC zoning management scheme, mapping where water quantity and water quality constraints bind and tailoring permit policy to each zone rather than imposing county-wide uniformity. Second, governments should develop standardized procedures for selecting among allocation methods, replacing ad hoc choices with a transparent evaluation of efficiency, fairness, and feasibility, so that enterprises and the public can understand why permits were distributed as they were. Third, the improved entropy weight method should be promoted for adoption, particularly for non-explicit high-environmental-cost industries like battery manufacturing, where no established allocation convention exists and the risk of arbitrary decisions is highest.

The significance of the study extends well beyond Changxing County. As global demand for batteries surges, manufacturing capacity is expanding rapidly in China, Southeast Asia, Europe, and North America, often in regions where water resources are already stressed. The battery industry thus sits at the intersection of two great transitions: the shift to clean energy and the need to protect freshwater ecosystems. This research offers a replicable framework, a dual-constraint assessment of water resources and environmental capacity, a menu of allocation methods tested against explicit performance criteria, and a standardized pathway for choosing among them. If adopted, it could help ensure that the factories powering the green transition do not quietly drain and degrade the rivers on which their host communities depend. In the end, the study’s message is that sustainability is not only about what we build, but about how precisely and fairly we manage the environmental limits within which everything must be built.

Subject of Research: Allocation of wastewater discharge rights for battery energy enterprises under dual water resource and water environmental capacity constraints

Article Title: Allocation of Discharge Rights for Non-Explicit High-Environmental-Cost Industries under the Dual Constraints of Water Resources Carrying Capacity and Water Environmental Capacity: Evidence from Changxing County, China

Article References: Du, H., Zhen, Z., Yang, C., Zhang, J., Zhu, J., Zheng, B., & Wan, W. (2026). Allocation of Discharge Rights for Non-Explicit High-Environmental-Cost Industries under the Dual Constraints of Water Resources Carrying Capacity and Water Environmental Capacity: Evidence from Changxing County, China. Water Resources Management, 40(13), Article 533. https://doi.org/10.1007/s11269-026-04896-6

Image Credits: AI Generated

DOI: 10.1007/s11269-026-04896-6

Keywords: discharge rights, water resources carrying capacity, water environmental capacity, battery industry, pollution permits, Changxing County, entropy weight method, data envelopment analysis, water quality, China, allocation methods, wastewater management