Impact of Production Tax Policy on Water Resource and Economy: A Case Study of Wenling City

Abstract

Rapid urbanization and industrialization have intensified the contradiction between water scarcity and economic growth. Achieving synergy between economic development and water conservation through taxation and subsidy policies has emerged as a critical research focus. This study develops an extended Computable General Equilibrium (CGE) model incorporating a water resource module to evaluate the impacts of production tax and subsidy policies in Wenling City, Zhejiang Province, China, a typical water-scarce city. By integrating a nested Constant Elasticity of Substitution (CES) production function for various water sources, the model captures the interactions between water supply and industrial output. Six policy scenarios of taxations and subsidies are designed. The impacts on macroeconomic aggregates, industrial output, and water usage are simulated. Results indicate that standalone taxation policies (Water Conservation Taxation Policy A1/Industrial Transformation Taxation Policy B1) reduce water usage by 3.35–3.80% but suppress Gross Domestic Product (GDP) growth by 0.37–0.76%. Among combined policies, the Water Conservation Combined Policy A3 achieves the optimal synergy between water conservation and economic growth, increasing real GDP by 1.00% while reducing water usage by 4.97%. This study reveals that taxation curbs the expansion of water-intensive industries, whereas subsidies redirect production factors toward water-efficient industries. Combining these policies effectively balances water conservation and economic development objectives. This study demonstrates how differentiated tax instruments drive water conservation through industrial transformation, providing a quantitative framework for production tax policy formulation in water-scarce regions.

1. Introduction

Rapid urbanization and industrialization have intensified water scarcity as a critical constraint on economic development. China—among the world’s most water-stressed nations—possesses per capita water resources amounting to merely one-third of the global average, with significant efficiency gaps persisting relative to high-income economies [1]. Conventional industrialization frequently triggers water resource overexploitation [2], while regulatory water conservation measures may simultaneously impede economic growth. The trilemma of economic expansion, industrial transformation, and water conservation has become increasingly acute [3]. Designing policy instruments that concurrently promote water conservation and economic growth thus constitutes an imperative for regional sustainability [4].

Extensive studies have examined the relationship between economic growth and water conservation, progressively expanding from agriculture and water-intensive industries to encompass specialized water use sectors across economic systems. Predominant methodologies include econometric models [5,6], system dynamics [7,8], and input–output models [9,10], addressing core themes such as water–economy nexus [11,12], water policies impacts [13,14], and synergies between water conservation and economic growth objectives [15].

Scholars widely acknowledge that measures such as water price adjustments [16], technological advancement [17], and industrial transformation [18] can effectively reduce water consumption and improve water use efficiency. Among these, industrial transformation is considered a fundamental strategy for achieving water conservation [19]. Existing studies demonstrate that sector-specific tax policies can guide the reallocation of production factors through price signals, thereby facilitating industrial transformation [20]. However, significant gaps remain in analyzing the impacts of specific water conservation policies targeting industrial transformation. In terms of research focus, existing studies often rely on water use coefficients and industrial structure analysis, failing to fully account for complex inter-industry linkages and varying water use characteristics [21]. In terms of research approach, most studies focus solely on assessing single-policy effectiveness, failing to adopt a holistic perspective to comprehensively evaluate the long-term impacts of policy portfolios [22]. Methodologically, while some scholars have employed Computable General Equilibrium (CGE) models to examine how water policies affect macroeconomic and industrial outputs, such models typically simplify water resources as exogenous variables within their frameworks [23]. This simplification overlooks sectoral differences in water use efficiency and their dynamic responses to policy interventions. As a consequence, conventional simulation approaches struggle to accurately quantify the integrated impacts of combined production tax and subsidy policies on regional water economy systems. Specifically, systematic modeling of taxing water-intensive industries or subsidizing emerging water-efficient sectors remains to be explored [24].

This study focuses on Wenling City, Zhejiang Province, China, a water-scarce region undergoing industrial transformation, as its study area. An analytical framework by constructing a CGE model with an extended water-resource module to assess the impacts of sectoral production tax policies is developed. Taxation and subsidy policies targeting distinct industrial sectors are simulated, delineating impact pathways on macroeconomic aggregates, sectoral outputs, and water usage.

2. Materials and Methods

2.1. Study Area

Wenling City is situated along the southeastern coast of Zhejiang Province, China (121°09′50″ E–121°44′0″ E, 28°12′45″ N–28°32′02″ N), covering a land area of 1073.6 km². Figure 1 shows the location of Wenling City. With a population of approximately 1.44 million, the city achieved a regional GDP of 141.7 billion CNY in 2024. Its economic structure is characterized by a tertiary sector ratio of 7.1:43.6:49.3 (primary:secondary:tertiary), with pillar industries, including the pump and motor sector, automotive/motorcycle sector, footwear/apparel sector, and machine tool sector.

Wenling City exemplifies resource-driven water scarcity, with its annual water resources averaging 962 million m³ and per capita availability less than one-third of China’s national average. The water supply relies predominantly on local water sources (Human Reservoirs, Taihu Reservoir, and river networks) and diverted water (the Changtan Water Diversion Project), while unconventional water utilization remains minimal. Governed by a subtropical monsoon climate, extreme precipitation seasonality frequently triggers spring/autumn droughts, intensifying water stress. Limited local water development potential forces heavy dependence on costly diverted supplies, creating acute water supply–demand imbalances.

Wenling currently navigates a critical transition phase in its industrial restructuring, where the dual imperatives of industrial transformation and water conservation reveal significant tensions. Water-intensive traditional industries such as papermaking and apparel continue to dominate industrial water consumption, while diversifying water demands from emerging sectors—including machinery manufacturing and electrical equipment production—exacerbate structural water stress. Despite progress, the region’s water use efficiency lags behind advanced water conservation cities, necessitating strategic policy interventions to optimize factor allocation and achieve sustainable equilibrium between economic growth and water resource sustainability.

As a representative coastal city grappling with resource-driven water scarcity, Wenling’s analysis of production tax policies holds substantial practical significance. Implementing differentiated measures—penalizing water-intensive industries while subsidizing water-efficient sectors—generates economic incentives that simultaneously curb inefficient water consumption, accelerate industrial upgrading, optimize water allocation efficiency, and alleviate regional supply–demand imbalances. The findings provide valuable insights for similar regions aiming to reconcile economic development with sustainable water resource utilization.

2.2. Model Structure

The Computable General Equilibrium (CGE) model is a policy analysis tool that describes interactions between supply, demand, and markets in an economic system [25]. By solving optimization equations—including producer profit maximization, consumer utility maximization, government budget equilibrium, and international market clearance [26]—the model generates equilibrium prices and quantities. The CGE model inherently captures how price fluctuations propagate across markets through cost transmission, substitution effects, and income effects. Compared with the input–output model (a commonly used economic analysis technique that employs an inter-sectoral flow matrix to depict interdependencies among sectors of an economy), CGE offers superior capability in simulating economy-wide responses to diverse policies [27], particularly through refined tax mechanisms that quantify differential impacts across sectors and commodities. Consequently, it provides a well-suited analytical framework for evaluating production tax policies.

This study employs a recursive dynamic CGE model with annual time stepping that explicitly captures intertemporal linkages. Capital is sector-specific and immobile within each period (vintage capital), following a putty–clay specification where existing capital stocks exhibit limited substitutability with other factors. The accumulation process is governed by:

$${\mathrm{Q}\mathrm{C}\mathrm{A}\mathrm{P}}_{\mathrm{t}+1,\mathrm{i}}={\mathrm{Q}\mathrm{C}\mathrm{A}\mathrm{P}}_{\mathrm{t},\mathrm{i}}\mathrm{\ast }\left(1-{\mathrm{D}\mathrm{E}\mathrm{P}}_{\mathrm{i}}\right)+{\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{V}\mathrm{E}\mathrm{S}\mathrm{T}}_{\mathrm{i}}$$

(1)

where ${\mathrm{Q}\mathrm{C}\mathrm{A}\mathrm{P}}_{\mathrm{t}+1,\mathrm{i}}$ denotes capital stock in sector i at period end, ${\mathrm{Q}\mathrm{C}\mathrm{A}\mathrm{P}}_{\mathrm{t},\mathrm{i}}$ is the opening stock, ${\mathrm{D}\mathrm{E}\mathrm{P}}_{\mathrm{i}}$ is the depreciation rate, and ${\mathrm{Q}\mathrm{I}\mathrm{N}\mathrm{V}\mathrm{E}\mathrm{S}\mathrm{T}}_{\mathrm{i}}$ is new investment.

While capital is fixed by sector in the short run, multi-year trajectories emerge through endogenous investment reallocation. Annual capital growth rates respond to sectoral rate-of-return differentials via capital supply curves, enabling gradual inter-sectoral capital mobility across periods. This specification reconciles short-term sectoral fixity with long-term structural change.

The model maintains a savings-driven closure, with the rate of return on capital treated exogenously while investment adjusts endogenously to available savings. Labor markets feature fixed real wages with endogenous employment and exogenous labor supply, capturing how policy shocks like tax changes propagate through sectoral restructuring and factor reallocation with full labor mobility. The household demand system adopts a Linear Expenditure System (LES) functional form, the Frisch parameter governs the elasticity of marginal utility of income, scaling subsistence consumption rigidities, while the consumer price elasticity quantifies demand responsiveness to price changes after accounting for these subsistence constraints. Government spending follows a fixed rule, and the exchange rate serves as the numeraire. Baseline trajectories are calibrated using historical trends for capital growth, labor supply, and productivity parameters.

To comprehensively model the intricate relationships between water use behaviors and economic activities, this study extends the CGE model with a water resource module, with the enhanced framework illustrated in Figure 2. Traditional CGE models treat water merely as a single sector and commodity, failing to reflect differentiated water-source-specific supply and demand responses to economic policies [23]. Addressing this gap, this study employs a nested Constant Elasticity of Substitution (CES) production function structure to systematically reflect substitution relationships among distinct water sources.

In the CGE model with the water resource module, the framework systematically examines substitution between local water and diverted water, capturing their measurable elasticity. These conventional sources are consequently integrated using a CES function as presented below:

$$X_{comwt,i}^{\left(1\right)}=CES\left\{{\displaystyle \frac{X_{(comwt,s),i}^{\left(1\right)}}{A_{(comwt,s),i}^{\left(1\right)}}};{\rho }_{comwt,i}^{\left(1\right)},b_{(comwt,s),i}^{\left(1\right)}\right\}(i=1,\dots ,n;s=\mathrm{1,2})$$

(2)

Equation (2) shows that the intermediate input of conventional water used in sector i ($X_{comwt,i}^{\left(1\right)}$) is the CES composite of local water and diverted water. The variable $X_{(comwt,s),i}^{\left(1\right)}$ represents the different sources of conventional water used in sector i. The variable s represents the source (s = 1 is local water; s = 2 is diverted water). The variable $A_{(comwt,s),i}^{\left(1\right)}$ represents the technological parameters; $b_{(comwt,s),i}^{\left(1\right)}$ represents the shares of different conventional water sources in sector i; and ${\rho }_{comwt,i}^{\left(1\right)}$ represents the constant elasticity of substitution between local water and diverted water, quantifying the ease with which producers can switch between these water sources in response to price changes, where higher absolute values indicate greater substitution flexibility.

The demand for “composite water” is represented as a CES equation as follows:

$$X_{water,i}^{\left(1\right)}=CES\left\{{\displaystyle \frac{X_{(water,s),i}^{\left(1\right)}}{A_{(water,s),i}^{\left(1\right)}}};{\rho }_{water,i}^{\left(1\right)},b_{(water,s),i}^{\left(1\right)}\right\}(i=1,\dots ,n;s=\mathrm{1,2})$$

(3)

Similar to Equation (2), Equation (3) shows that composite water used in sector i ($X_{water,i}^{\left(1\right)}$) is the CES composite of conventional water and unconventional water. The variable $X_{(water,s),i}^{\left(1\right)}$ represents the different sources of water used in sector i; and s represents the source (s = 1 is conventional water; s = 2 is unconventional water). The variable $A_{(water,s),i}^{\left(1\right)}$ represents the technological parameters; $b_{(water,s),i}^{\left(1\right)}$ represents the shares of different water sources in sector i; and ${\rho }_{water,i}^{\left(1\right)}$ is the constant elasticity of substitution between conventional water and unconventional water.

The extended water resource module facilitates its applicability to diverse geographical contexts, as the model can be readily adapted through the recalibration of economic structure, water source types, and substitution elasticity parameters to reflect regional characteristics.

2.3. Model Database

The basic data of the developed model primarily encompass economic and water use statistics. Economic data are compiled from the 42-sector input–output tables of Zhejiang Province for 2017 [28] and the “Wenling Statistical Yearbook 2023” published by the local statistics bureau [29], providing information on inter-sectoral input structures, value added compositions, final consumption patterns, and trade flows. Water use data are compiled from the “2023 Water Resources Bulletin” published by the local water resources bureau [30], providing information on water usage by sectors and supply by water sources.

Based on the economic and water use data, a model database for the study area was developed. The input–output table was updated using the bi-proportional scaling method according to sectoral value added, household consumption, and import-export statistics from the 2023 Statistical Yearbook of Wenling. Industries with similar water use characteristics were consolidated, ensuring internal consistency in water intensity and policy responsiveness within aggregated sectors, while retaining water-intensive sectors (e.g., papermaking, chemicals), yielding 18 commodity and sector classifications, as shown in

Table 1. Model sectors and water use intensity (cubic meters per CNY 1000 of value added).

Model Sectors (Abbreviated Names)	National Economy Industry Classification	Water Use Intensity
Agriculture (Agri)	Agriculture, forestry, animal husbandry, and fishery	21.32
Food Processing (Food)	Food and tobacco processing	3.00
Clothes and Textile (Cloth)	Manufacture of leather, fur, feather, and related products	1.68
Paper Products (Paper)	Manufacture of paper, printing, articles for culture, education, and sport activity	2.78
Chemical Products (Chemi)	Manufacture of chemical products	3.49
Metal Products (Metal)	Manufacture of metal products	2.18
General Equipment (Geqpm)	Manufacture of general purpose machinery	1.58
Transport Equipment (Trneq)	Manufacture of transport equipment	0.54
Electrical Equipment (Elceq)	Manufacture of electrical machinery and equipment	1.02
Other Manufacturing (Othin)	Mining and washing of coal; extraction of petroleum and natural gas; mining and processing of metal ores; mining and processing of nonmetal and other ores; food and tobacco processing; textile industry; manufacture of leather, fur, feather, and related products; processing of timber and furniture; manufacture of paper, printing, articles for culture, education, and sport activity; processing of petroleum, coking, processing of nuclear fuel; manufacture of chemical products; manuf. of non-metallic mineral products; smelting and processing of metals; manufacture of metal products; manufacture of general purpose machinery; manufacture of special purpose machinery; manufacture of transport equipment; manufacture of electrical machinery and equipment; manufacture of communication equipment, computers, and other electronic equipment; manufacture of measuring instruments; other manufacturing and comprehensive use of waste resources; repair of metal products, machinery, and equipment; production and distribution of electric power and heat power; production and distribution of gas	2.76
Conventional Water (Comwat)	Production and distribution of water	2.26
Diverted Water (Divwat)	Production and distribution of water	1.29
Recycled Water (Rcywat)	Production and distribution of water	21.95
Construction (Cnstr)	Construction	0.91
Trade (Trade)	Wholesale and retail trades	0.10
Transportation (Trans)	Transport, storage, and postal services	0.22
Normal Services (Nrmsv)	Information transfer, software, and information technology services; finance; real estate; leasing and commercial services; research and development; polytechnic services; administration of water, environment, and public facilities	0.76
Water-Intensive Services (Wstsv)	Accommodation and catering; resident, repair, and other services; education; health care and social work; culture, sports, and entertainment; public administration, social insurance, and social organizations	1.30

. Notably, the “production and distribution of water” sector was decomposed into three distinct water supply sectors (conventional water, diverted water, and recycled water) according to water usage by water sources, accurately reflecting the multi-source water supply structure in Wenling City.

Cross-validation was implemented through sector-specific approaches to ensure data accuracy: industrial water use was calibrated by matching above-scale enterprise water consumption records with sectoral totals; agricultural water use was verified against irrigated areas, crop patterns, and measured abstraction from irrigation districts, collectively establishing an integrated database for water economic analysis.

2.4. Model Parameters

Model parameters governing economic behaviors—including production technologies and consumption preferences [31]—encompass substitution elasticities (e.g., capital–labor substitution in CES functions) and technological scale parameters characterizing input substitutability and production efficiency; utility function parameters (e.g., Cobb–Douglas expenditure shares) reflecting consumer preference intensities; trade elasticities (Armington elasticities for import domestic substitution, export demand elasticities for price quantity responses); fiscal policy instruments (tax/subsidy rates) affecting government budgets and market equilibrium; initial factor endowments (labor supply, capital stock) determining economic scale under resource constraints; and macroeconomic parameters (savings/investment rates) essential for capital market and fiscal balance [32].

Parameters were assigned due to empirical evidence and analytical findings. The labor elasticity of the demand coefficient was set to 0.243 according to the estimation from the Chinese Academy of Social Sciences [33]. The consumer price elasticity were set to 4.0, referring to the People’s Republic of China’s General Equilibrium Model (PRCGEM) model [34]. The Armington elasticity, factor substitution elasticity, and household consumption elasticity were set according to the CHINAGEM model database [35], with values ranging from 1.9 to 5.2. The water substitution elasticity was set according to the SICGE model database [33], with values ranging from 0.2 to 1.0. The Frisch parameters were assigned to −2.0 according to the per capita income level of Wenling based on the literature [36]. The remaining parameters were also adopted from the ORANI-G model database.

Model validation includes data balance test, closure test, homogeneity test, and robustness test.

(1)

The data balance test ensures precise replication of benchmark year economic accounts. Results indicate that the model accurately replicates the economic accounts, confirming the precise alignment between the model database and the actual economic structure of Wenling City.

(2)

The closure test guarantees model solvability by aligning the quantitative relationship between exogenous and endogenous variables. Results indicate that under the baseline closure condition, the model has a total of 4346 exogenous variables and 6275 endogenous variables, with a corresponding number of 6275 equations. The dimensions of all endogenous variables are consistent with the equations, and the model passes the closure rule test.

(3)

The homogeneity test validates conformance with general equilibrium theory principles. Nominal homogeneity examines “money neutrality” by exogenously scaling all benchmark prices (e.g., +1%) and verifying proportional price changes alongside invariant quantities; real homogeneity confirms constant returns to scale by exogenously scaling quantity variables (e.g., capital, labor) and verifying proportional quantity changes with stable prices. The homogeneity test results are shown in

Table 2. Homogeneity test results.

	Nominal Homogeneity Test (%)	Real Homogeneity Test (%)
Real GDP	0.001	1.000
Real Investment Expenditure	0.000	1.000
Real Household Consumption	0.001	1.000
GDP Price Index	0.999	0.001
Investment Price Index	1.000	0.001
Consumer Price Index	1.000	0.000

. Results indicate that in the nominal homogeneity test, the model’s price variables changed by approximately 1%, while the quantity variables remained unchanged. In the real homogeneity test, the model’s quantity variables changed by approximately 1%, while the price variables remained stable, and the model passes the homogeneity test.

(4)

The robustness test evaluates the impact of parameter uncertainty on model outcomes. Following Mahmood [37]’s methodology, we generated a uniform distribution series of elasticity parameters for the production function (including Armington elasticity, factor substitution elasticity, household consumption elasticity, water substitution elasticity ρ(1)comwt,i and ρ(1)water,i), ranging from low elasticity (reduced by 10%) to high elasticity (increased by 10%). These alternative parameter sets were systematically substituted into the model to examine variations in key output variables. As presented in

Table 3. Robustness test results.

Model Parameters	Value Added (Billion CNY)		Water Usage (Million m3)
	Mean	Standard Deviation	Mean	Standard Deviation
Initial Value	98.5173	-	262.1000	-
Armington Elasticity	98.5169	0.0113	262.0252	0.2872
Factor Substitution Elasticity	98.5214	0.0153	262.1209	0.0762
Household Consumption Elasticity	98.5173	0.0073	262.0973	0.0909
Water Substitution Elasticity ${\rho }_{comwt,i}^{\left(1\right)}$	98.5169	0.0070	262.0999	0.0144
Water Substitution Elasticity ${\rho }_{water,i}^{\left(1\right)}$	98.5173	0.0034	262.1011	0.0104

, the standard deviations of value added across all parameter variations remained below CNY 0.02 billion, the standard deviations of water usage across all parameter variations remained below 0.3 million m³, indicating that the main policy conclusions are robust to reasonable parameter fluctuations.

The developed CGE model passed the balance test, closure test, homogeneity test, and robustness test, confirming its stability and reliability. This rigorous validation process established that the model is fully capable of simulating the impacts of production tax policies.

2.5. Policy Scenarios

Based on the developed CGE model, two sets of policy scenarios were designed along distinct dimensions: water conservation policies (water-intensive vs. water-efficient industries) and industrial transformation policies (traditional vs. emerging industries). Group A focused on water use efficiency, implementing a 5% production tax on water-intensive industries (A1), a 5% production subsidy for water-efficient industries (A2), and a combined policy integrating both measures (A3). Meanwhile, Group B emphasized industrial transformation, featuring a 5% production tax on traditional industries (B1), a 5% production subsidy for emerging industries (B2), and a combined policy merging both approaches (B3). Policy scenario details are shown in

Table 4. Policy scenarios.

Policy Number	Policy Name	Policy Content	Target Sectors
A1	Water Conservation Taxation Policy	+5% tax on water-intensive industries	Agriculture, Food Processing, Paper Products, Chemical Products, Water-Intensive Services
A2	Water Conservation Subsidy Policy	−5% subsidy on water-efficient industries	Clothes and Textile, Transport Equipment, Electrical Equipment, Construction, Trade, Transportation, Normal Services
A3	Water Conservation Combined Policy	+5% tax on water-intensive industries, −5% subsidy on water-efficient industries	A1 + A2
B1	Industrial Transformation Taxation Policy	+5% tax on traditional industries	Agriculture, Food Processing, Clothes and Textile, Other Manufacturing, Construction Electrical Equipment, Normal Services
B2	Industrial Transformation Subsidy Policy	−5% subsidy on emerging industries	General Equipment, Transport Equipment, Electrical Equipment, Normal Services
B3	Industrial Transformation Combined Policy	+5% tax on traditional industries, −5% subsidy on emerging industries	B1 + B2

.

3. Results and Discussion

Based on the CGE model featuring an extended water resource module, the impacts of six tax and subsidy policies targeting industries were simulated for macroeconomic outcomes, sectoral output, and water usage, providing a quantitative basis for synergizing water conservation with economic growth.

3.1. Model Results

3.1.1. Macroeconomic Impact

• Real GDP Impact

Based on simulation results, the six policy scenarios exhibit significantly divergent impacts on Wenling City’s real GDP. Figure 3 shows the cumulative impact of real GDP under the different policy scenarios, which is calculated by summing the annual percentage deviations relative to the baseline scenario from the start year to the end year.

For taxation policies, the water conservation taxation policy (A1) initially imposes a strong negative shock to GDP (−2.27% in 2025), but this adverse effect progressively weakens to −0.37% by 2030 as industrial transformation takes hold. Conversely, the industrial transformation taxation policy (B1) demonstrates more persistent suppression, with its negative impact declining slowly from −2.17% in 2025 to −0.76% in 2030.

Subsidy policies significantly stimulate economic growth: the water conservation subsidy policy (A2) initially generates an immediate positive growth of 4.03%, though this increment diminishes over time; similarly, industrial transformation subsidy policy (B2) initially boosts GDP by 2.31%, stabilizing around 0.7% later in the policy cycle.

Among combined policies, the water conservation combined policy (A3) achieves sustained moderate growth (annual average 1.16%), rebounding to 1.0% in 2030 through counteracting effects. In contrast, the industrial transformation combined policy (B3) shows neutralizing effects, with the marginal initial growth (0.08%) turning negative and stabilizing at −0.19% by 2030.

Overall, the water conservation subsidy policy delivers the strongest economic stimulus, while the water conservation taxation policy exhibits the most pronounced negative drag effect.

2.

Employment Impact

Figure 4 shows the cumulative impact of employment under the different policy scenarios.

For taxation policies, policy A1 exerts sustained negative pressure on employment, with a cumulative decline of −4.64% by 2025. However, as industrial transformation progresses, this cumulative adverse effect gradually weakens, narrowing to −0.7% by 2030. Similarly, policy B1 results in a relatively moderate cumulative employment suppression effect of −4.27% by 2025, though the downward trend persists until the end of the policy cycle with a cumulative decline of −0.81% by 2030.

Subsidy policies significantly boost cumulative employment: policy A2 generates a cumulative growth of 8.87% by 2025, though this cumulative growth diminishes over time to 2.24% by 2030; policy B2 also shows a declining cumulative effect, with cumulative growth falling from 5.52% by 2025 to 1.31% by 2030, indicating diminishing policy effectiveness over time for both subsidy types.

Among combined policies, policy A3 achieves sustained cumulative employment growth through policy synergy, reaching a cumulative growth of 4.1% by 2025 and maintaining a positive cumulative increase of 1.38% by 2030. Conversely, policy B3 exhibits mild cumulative growth characteristics, starting with a cumulative growth of 1.2% by 2025 but gradually slowing to a cumulative growth of 0.39% by 2030, reflecting how the offsetting effects of taxation and subsidies weaken overall policy impact.

Overall, the water conservation subsidy policy provides the strongest cumulative employment stimulus by the early policy period, while the water conservation taxation policy has the most pronounced cumulative suppression effect by the early policy period. Combined policies, through structural adjustment, effectively mitigate the cumulative employment shock of single taxation measures while enhancing policy sustainability.

3.

Factor Income Impact

Figure 5 shows the cumulative impact of factor income under the different policy scenarios.

For taxation policies, policy A1 initially exerts significant negative pressure, reducing factor income by 2.31% in 2025. However, this adverse effect gradually diminishes—reaching −0.57% by 2030—as industrial transformation and technological substitution take effect. In contrast, policy B1 shows more persistent suppression, with factor income declining from −2.11% in 2025 to −0.72% in 2030, reflecting long-term drag effects from sectoral contraction.

Subsidy policies substantially boost factor income: policy A2 delivers a strong initial growth of 4.02%, maintaining a positive 1.44% by 2030 despite diminishing returns. Similarly, policy B2 sees gains decline from 2.48% to 0.54%, indicating marginal decreases in resource demand.

Among the combined policies, policy A3 achieves sustained growth through structural optimization, with factor income rising steadily from 1.85% to 0.81% (2030), demonstrating synergy between water savings and income gains. Conversely, policy B3 displays policy offset effects—initial marginal growth (0.48%) turned negative (−0.29% by 2030)—highlighting risks of resource misallocation during sectoral shifts.

Overall, the subsidy for water-efficient industries provides the strongest factor income uplift, while the taxation of traditional industries inflicts the most pronounced adverse impact.

3.1.2. Sectoral Value Added Impact

Based on simulation results, the six policy scenarios exhibit significant heterogeneity in their impacts on sectoral value added, with particularly pronounced variations among water-intensive industries, traditional sectors, and emerging industries. Figure 6 shows the impact of sectoral value added under different policy scenarios, representing the absolute differences between these scenarios and the baseline scenario for 2030. These impacts are calculated by applying annual percentage changes derived from the CGE model simulations to the baseline value added levels.

For taxation policies, policy A1 results in substantial reductions in value added for targeted sectors such as agriculture (CNY −6.68 billion) and food processing (CNY −0.76 billion), while non-water-intensive sectors like apparel/footwear (CNY +1.64 billion) and construction (CNY −1.59 billion) show divergent responses, reflecting direct regulatory effects on water efficiency. Within policy B1, apparel/footwear (CNY −6.39 billion) and construction (CNY −6.76 billion) experience significant declines, though paper/printing (CNY +0.38 billion) grows despite not being directly restricted.

For subsidy policies, policy A2 stimulates substantial growth in construction (CNY +18.60 billion), with general services (CNY +4.99 billion) and electrical machinery manufacturing (CNY +1.48 billion) also expanding, while water-intensive sectors like agriculture (CNY −3.01 billion) and paper/printing (CNY −0.56 billion) passively contract, highlighting the subsidy’s role in redirecting production factors. Policy B2 drives growth in general equipment manufacturing (CNY +6.86 billion) and transportation equipment manufacturing (CNY +2.62 billion), with construction (CNY +8.41 billion) benefiting indirectly.

Combined policies demonstrate amplified effects: policy A3 boosts apparel/footwear (CNY +7.08 billion) and construction (CNY +16.66 billion), while agriculture (CNY −9.36 billion) and water-intensive services (CNY −5.18 billion) see expanded declines, indicating reinforced structural adjustments. Policy B3 delivers the largest growth in general equipment manufacturing (CNY +9.11 billion), with construction (CNY +0.71 billion) stabilizing, illustrating how policy counterbalancing moderates industrial substitution. Notably, construction—a pivotal intermediate input sector—exhibits robust growth resilience under both A2 and B2 policies, underscoring its central role in policy transmission. Agriculture persistently contracts across multiple scenarios (A1: CNY −9.36 billion; A2: CNY −3.01 billion; B3: CNY −9.23 billion), confirming stable policy suppression of water-intensive sectors.

Overall, water conservation policies drive structural adjustments through curbing water-intensive sectors and incentivizing water-efficient ones, while industrial transformation policies emphasize industrial substitution via traditional sector contraction and emerging industry expansion.

3.1.3. Sectoral Water Usage Impact

Figure 7 shows the impact of sectoral water usage under the different policy scenarios.

For taxation policies, regarding policies A1, agricultural water consumption decreases by 10.54 million m³, while water-intensive industries like food processing and paper/printing reduce usage by 0.16 million m³ and 0.29 million m³, respectively, indicating targeted suppression effects. However, apparel/footwear water usage increases by 0.17 million m³ and construction decreases by 0.08 million m³, revealing divergent indirect effects on non-water-intensive sectors. Policy B1 reduces water usage in apparel/footwear by 0.66 million m³ and construction by 0.36 million m³, but increases paper/printing usage by 0.08 million m³, reflecting varied responses within traditional industries.

For subsidy policies, Policy A2 increases construction water usage by 0.99 million m³, water-intensive services by 0.27 million m³, and electrical machinery by 0.11 million m³, while agriculture decreases by 4.74 million m³, demonstrating how subsidies stimulate water-efficient sectors while accelerating contraction in water-intensive industries. Policy B2 boosts water usage in general equipment manufacturing by 0.79 million m³, transportation equipment and electrical machinery by 0.11 million m³ and 0.11 million m³, respectively, with construction increasing by 0.45 million m³ due to intermediate demand, highlighting spillover effects from emerging industry development. Nevertheless, as the water intensity of the construction sector is relatively low, this increase does not offset the overall water conservation effects.

The combined policies intensify outcomes: policy A3 expands agricultural water reduction to 14.76 million m³ while increasing apparel/footwear and construction usage by 0.73 million m³ and 0.89 million m³, showcasing coordinated mechanisms for dual water economic regulation. Policy B3 raises general equipment manufacturing usage by 1.04 million m³ but reduces apparel/footwear by 1.08 million m³, with construction only minimally increasing (0.04 million m³). Overall, water conservation policies achieve more immediate water savings by suppressing water-intensive sectors, while industrial transformation policies reconfigure water use through emerging industry development, requiring trade-offs between short-term conservation and long-term structural adaptation.

3.2. Policy Scenario Comparison

Table 5. Policy scenario comparison.

Policy Name	A1	A2	A3	B1	B2	B3
Real GDP %	−0.37%	1.58%	1.00%	−0.76%	0.70%	−0.19%
Employment %	−0.70%	2.24%	1.38%	−0.81%	1.31%	0.39%
Factor Income %	−0.57%	1.44%	0.81%	−0.72%	0.54%	−0.29%
Water Usage %	−3.80%	−1.30%	−4.97%	−3.35%	−1.74%	−5.07%
Water Efficiency %	−3.44%	−2.84%	−5.91%	−2.61%	−2.43%	−4.89%

Note: Real GDP, employment, and factor income values represent cumulative percentage deviations from the baseline scenario by the 2030 endpoint. Water usage and water efficiency values represent simple percentage deviations from the baseline scenario at the 2030 endpoint.

shows the policy scenario comparison. For macroeconomic impact, Policy A2 delivers the most substantial GDP boost (1.58%) among single policies, while Policy B1 exhibits the strongest inhibitory effect (−0.76%). Among the combined policies, A3 achieves moderate growth (1.00%) through tax/subsidy counterbalancing, outperforming B3. Employment impacts aligned with economic trends: Policy A2 increases employment by 2.24%, whereas A1 and B1 cause declines of 0.7% and 0.81%, respectively. Combined Policy A3 (1.38%) similarly surpasses B3 (0.39%) in employment gains. Regarding factor income, Policy A2 increases returns by 1.44%, while B1 (−0.72%) and B3 (−0.29%) reflect negative impacts from traditional sector contraction. Water usage analysis reveals significant conservation effects under standalone taxation Policies A1 (−3.8%) and B1 (−3.35%). Among the combined approaches, Policy B3 achieves the maximal water reduction (−5.07%) through industrial substitution, with Water Conservation Policy A3 (−5.91%) and Industrial Transformation Policy B3 (−4.89%) showing optimal efficiency improvements.

Overall, water conservation policies simultaneously enable water savings and economic growth via targeted interventions, whereas industrial transformation policies enhance long-term water conservation effectiveness, though requiring careful equilibrium between traditional sector contraction and emerging industry cultivation.

3.3. Comparison with Previous Studies

Building upon prior research, this study develops an extended CGE model incorporating a water resource module to validate the water economic impacts of production tax policies, exploring sectoral differential responses and synergistic effects of policy combinations. This approach offers a comprehensive analytical framework for water economy policy assessment.

In terms of research methods, conventional CGE models often oversimplify water resources as exogenous variables or homogeneous commodities, failing to capture substitution elasticities between diverse water sources (local, diverted, and unconventional water) and their impacts on sectoral cost structures [23]. Our enhanced model precisely characterizes substitution relationships between local and diverted water, as well as between conventional and unconventional sources, enabling accurate simulation of how tax/subsidy policies redirect production factors across sectors through price signals. This advancement overcomes limitations of traditional approaches reliant on simplified water use coefficients and industrial structure analyses [38].

In terms of research approach, this study employs multi-scenario simulation to systematically quantify the synergistic effects of diverse policy combinations. Comparing with single-policy studies [39], this study reveals the economic water conservation synergy potential of combined tax/subsidy approaches—a finding consistent with Wu et al.’s [24] conclusions from open-water CGE modeling. Notably, our analysis demonstrates that the combined tax/subsidy approach achieves superior outcomes in balancing water conservation with economic growth, effectively addressing the implementation gap identified in recent policy evaluations [40].

In terms of the research findings, beyond macro-level tax–GDP relationships explored in the existing literature, our work further quantifies sectoral water output characteristics, providing actionable insights for designing industrial production tax policies. Our findings corroborate pervious research that water resource tax policies present complex trade-offs between economic development and ecological protection, though our granular sectoral analysis offers more precise implementation pathways [41].

3.4. Limatations

While the present study offers valuable insights into the impacts of production tax policies on water resource and economic growth, several limitations warrant acknowledgment to guide future research and practical applications.

A limitation of this study is its omission of the dynamic impacts of water-saving technological progress. Future research should integrate technological dynamics into CGE models to assess synergies between policy instruments and innovation for sustainable water management.

Another significant limitation concerns the uniform tax/subsidy rate applied across policy scenarios. This rate was selected based on preliminary assessments of regional fiscal feasibility, providing a standardized basis for comparative analysis. However, this represents a simplification that merits further investigation. Future studies should conduct comprehensive sensitivity analyses across a wider range of policy intensities (e.g., 1−10%) to determine optimal intervention levels.

Furthermore, this study’s exclusive focus on production tax and subsidy mechanisms presents a constrained policy landscape that may not fully capture real-world implementation complexities. Future research should develop multi-instrument frameworks that simultaneously model tax policies alongside water pricing structures and tradable permit systems to better reflect the policy portfolios typically employed by water-stressed regions.

Additionally, the net fiscal burden of the combined policy for government budgets has not been assessed. Future research should incorporate fiscal accounting mechanisms to evaluate budgetary implications of policy combinations, providing practical guidance for policymakers with constrained fiscal capacity.

4. Conclusions

This study employs an extended CGE model incorporating a water resource module to systematically evaluate the impacts of production tax and subsidy policies on economic and water resource in Wenling City. It further reveals synergistic mechanisms under differentiated policy approaches. Key findings include the following:

(1)

Taxation policies effectively curtail water demand (A1: −3.80%; B1: −3.35%) but cause short-term economic contraction (A1 GDP: −0.37%; B1 GDP: −0.76%) while reducing employment and factor income.

(2)

Subsidy policies also suppress water usage (A2: −1.30%; B2: −1.74%) while stimulating the economy (A2 GDP: +1.58%; B2 GDP: +0.70%), though potentially triggering an increase in water consumption in intermediate sectors like construction. This may partially offset water-saving benefits in the short term but still achieves net water savings overall.

(3)

The combined Water Conservation Policy A3 demonstrates water economy synergy (GDP: +1.00%; water: −4.97%)—taxation constrains water-intensive expansion through production costs, while subsidies redirect factors to water-efficient industries, yielding dual benefits of industrial optimization and efficiency gains. The model indicates that the combination of taxation on water-intensive industries and subsidies for water-efficient sectors creates synergistic effects—taxation constrains water-intensive expansion through production costs, while subsidies redirect factors to water-efficient industries, yielding dual benefits of industrial optimization and efficiency gains.

This study quantifies economic water impacts of taxation and subsidy policies and identifies water conservation pathways through industrial transformation, offering evidence-based decision support for water-scarce regions.