Policy Uncertainty Reduces Green Innovation: Wang, Wurgler & Zhang (2026)

What this is. The paper’s core results, the simple mean-variance model of investment under uncertain subsidies, and the two-stage IV design that instruments policy uncertainty with weather volatility: enough to understand what was found and how, without reading all 14 pages. To replicate or extend, read the full source at the original.

TL;DR

The paper documents that policy uncertainty about environmental subsidies suppresses the green R&D that those subsidies are designed to stimulate. The setting is China, where provincial and local governments allocate environmental subsidies partly on the basis of Air Quality Index (AQI) readings, and weather (wind and rain) drives AQI in predictable but noisy ways. Weather variability over a rolling six-year window therefore generates exogenous variation in how predictable a city’s subsidy allocation will be, forming an instrument for policy uncertainty. A one-standard-deviation increase in weather variability reduces city-level green R&D by 0.132 standard deviations (IV estimate, Table 4 Model 5, p. 9). Effects are stronger for green-tech firms, subsidy-reliant firms, and firms under financial pressure. Placebo tests on non-green R&D and capital expenditure show no significant effect, consistent with the channel being specific to subsidized green activities. The paper extends Gulen and Ion (2016) by identifying a behavioral source of policy uncertainty: policymakers respond to salient recent pollution readings, which are themselves influenced by transient weather, creating a recency-bias channel that Bernanke (1983) and Dixit and Pindyck (1994) frameworks predict will deter investment.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Policy uncertainty (IV) reduces city-level green R&D	Table 4 Model 5, p. 8	PU coefficient = -2.23*** (t = -2.64); one-SD weather variability reduces green R&D by 0.132 SD; OLS comparison: -0.29** (Table 4 Model 1)
R2	Higher average subsidies stimulate green R&D	Table 4 Model 5, p. 8	Avg Green Subsidy = 0.34*** (t = 2.76); one-SD subsidy increase raises green R&D by 0.158 SD
R3	Policy uncertainty reduces green R&D employment	Table 8 Model 1, p. 12	PU coefficient = -1.07*** (t = -3.41); one-SD weather variability reduces employment by 0.153 SD
R4	Green-tech firms are more sensitive to policy uncertainty	Table 6 Model 2, pp. 10-11	PU × Green Tech = -0.58*** (t = -7.47), adding to average-firm effect of -0.40*** (Table 6 Model 1, t = -2.72)
R5	Subsidy-reliant firms are more sensitive to policy uncertainty	Table 6 Model 5, p. 11	PU × Subsidy Reliant = -0.62*** (t = -7.77)
R6	Policy uncertainty does not affect non-green R&D or capital expenditure (placebo)	Table 5 Models 6-8, p. 10	Non-green R&D: PU = 0.83 (t = 1.48, n.s.); capex: PU = -0.00 (t = -0.44, n.s.); real estate capex: PU = 0.03 (t = 0.32, n.s.)
R7	Higher AQI predicts higher environmental subsidies	Table 2 Model 2, p. 6	AQI = 0.05*** (t = 4.88); one-SD higher AQI predicts 0.17 SD higher next-year subsidy
R8	Higher AQI predicts higher waste gas treatment spending	Table 2 Model 5, p. 6	AQI = 0.04*** (t = 5.08); one-SD higher AQI predicts 0.13 SD higher expenditure
R9	Wind and rain reduce seasonal AQI	Table 3 Models 1-4, p. 7	Wind: -3.15*** (t = -3.52), -2.63*** (t = -3.48), -5.13*** (t = -3.09), -2.34* (t = -1.78); rain: -2.62*** (t = -4.82), -1.73*** (t = -4.62), -1.60 (t = -0.49), -3.33** (t = -2.41), for heating/non-heating seasons in all/northern cities
R10	Weather variability predicts policy uncertainty in the first stage	Table 4 Model 2, p. 8	SD Windy Days = 0.01*** (t = 3.27); SD Rainy Days = 0.16*** (t = 3.71); first-stage F = 14.5; one-SD variability raises PU by 0.228 SD
R11	Green R&D effects persist across robustness specifications	Table 5 Models 1-5, p. 10	PU = -2.98** (t = -2.43), -2.23*** (t = -2.59), -2.22*** (t = -2.63), -2.25*** (t = -2.69), -5.14*** (t = -3.17), across nonzero-R&D, two-year AQI, weather-adjusted AQI, expanded controls, and 2013-2019 sample
R12	Policy uncertainty has stronger effects in pollution-related industries	Table 6 Models 8-11, p. 11	PU interactions: manufacturing = -0.78*** (t = -10.13), chemical = -0.76*** (t = -5.07), metal = -0.92*** (t = -4.44), public facility = -0.91** (t = -2.52)
R13	Several other firm types show no significant differential effect	Table 6 Models 3-7, p. 11	PU interactions: metropolitan = -0.07 (t = -0.73), large = 0.11 (t = 0.71), SOE = -0.04 (t = -0.46), different cities = -0.01 (t = -0.10), all n.s.
R14	Financial pressure increases green-tech firms’ sensitivity to uncertainty	Table 7 Models 2-4, p. 12	PU × Green Tech × Over-indebtedness = -0.55** (t = -2.09); × Interest Coverage A = 0.30* (t = 1.81); × Interest Coverage B = 0.33* (t = 1.91); base PU × Green Tech = -0.60*** (t = -7.11)
R15	Higher subsidies predict higher green R&D employment	Table 8 Models 2-3, p. 12	Avg Green Subsidy = 0.16*** (t = 3.61), Model 2; 0.15*** (t = 3.42), Model 3
R16	Green subsidies rose after Beijing’s severe pollution episode	text p. 8	Environmental subsidies to Beijing-headquartered firms increased by 78% in the following year after the November 19, 2010 AQI spike

Overall (paper’s conclusion). Policy uncertainty about environmental subsidies has real effects on green innovation: it reduces both the R&D investment and the technical employment that subsidies are intended to promote. Effects concentrate on the firms theory predicts should be most sensitive (green-tech, subsidy-reliant, financially pressured), and placebos confirm the result is not a general investment or uncertainty shock but is specific to the green component tied to the subsidized activities.

Theory / model

The paper develops a stylized mean-variance model to derive qualitative predictions about how subsidy uncertainty affects green investment relative to traditional investment (Section 2.2, p. 4). A firm raises capital to invest in two technologies at the start of a period. The first, $G$, is an environmental technology receiving an uncertain government subsidy $S \sim N(\bar{S}, \sigma_S^2)$. The total per-dollar payoff from $G$ is $\bar{R}_G + \bar{S}$ with variance $\sigma_G^2 + \sigma_S^2$. The second, $X$, is a traditional technology with per-dollar payoff $\bar{R}_X \sim N(\bar{R}_X, \sigma_X^2)$. Both investments are subject to a gross financing cost $C$. The firm maximizes mean-variance utility (equation 1, p. 4):

$$\max_{G,X} \; G(\bar{R}_G + \bar{S} - C) - \frac{\gamma}{2}G^2(\sigma_G^2 + \sigma_S^2) + X(\bar{R}_X - C) - \frac{\gamma}{2}X^2\sigma_X^2 \tag{1}$$

where $\gamma$ is risk aversion, which stands in for irreversibility, adjustment costs, or managerial risk aversion in the spirit of Bernanke (1983), Dixit and Pindyck (1994), and Julio and Yook (2012). The first-order conditions yield optimal investments (equation 2, p. 4):

$$G^* = \frac{\bar{R}_G + \bar{S} - C}{\gamma(\sigma_G^2 + \sigma_S^2)}, \qquad X^* = \frac{\bar{R}_X - C}{\gamma\sigma_X^2} \tag{2}$$

The ratio of green to traditional investment is (equation 3, p. 4):

$$\frac{G^*}{X^*} = \frac{\bar{R}_G + \bar{S} - C}{\bar{R}_X - C} \times \frac{\sigma_X^2}{\sigma_G^2 + \sigma_S^2} \tag{3}$$

The model yields three comparative statics that the paper tests empirically:

1. Higher subsidy policy uncertainty $\sigma_S^2$ reduces both the level $G^*$ and the share $G^*/X^*$.
2. Higher average subsidies $\bar{S}$ increase the level and share of green R&D.
3. Higher financing cost $C$ (a proxy for financial pressure or over-indebtedness) increases the sensitivity of $G^*/X^*$ to $\sigma_S^2$, predicting that financially constrained firms are more affected by policy uncertainty.

This is not a structural model; it is a stylized device for generating falsifiable comparative statics. The paper also discusses irreversibility and adjustment-cost channels and a recency-bias channel, in which policymakers over-weight recent salient pollution conditions and under-account for transient weather, creating avoidable policy noise.

Identification logic. The empirical chain is: weather volatility drives AQI volatility, which drives subsidy volatility, which creates policy uncertainty that depresses green R&D. The exclusion restriction is that weather volatility affects green R&D only through this subsidy-uncertainty channel, not through other routes such as direct productivity effects of air quality on R&D effort. Placebos against non-green R&D and capex (R6) support this restriction.

Method

Step 1: Subsidies depend on AQI. To show that green subsidy allocations respond to observed pollution, the paper estimates a Fama and MacBeth (1973) cross-sectional regression of city-level green subsidy on lagged AQI (equation 4, p. 6):

$$\text{Green Subsidy}_{jt} = a_0 + a_1 \times AQI_{j,t-1} + A \times X_{j,t-1} + \varepsilon_{jt} \tag{4}$$

where $\text{Green Subsidy}_{jt}$ is the log RMB of total environmental subsidies in city $j$ in year $t$, $AQI_{j,t-1}$ is the lagged average AQI, and $X_{j,t-1}$ contains city characteristics and asset-weighted firm controls where included. Coefficients are averaged across cross-sections with standard errors robust to cross-sectional correlation; the Table 2 sample covers 2003-2019 and up to 352 cities. Models 5-6 substitute city waste-gas-treatment expenditure as the outcome, providing an independent policy response check. A one-SD higher AQI is associated with a 0.17 SD higher subsidy the following year (Table 2 Model 2, coefficient = 0.05***, t = 4.88, p. 6).

Step 2: AQI depends on weather. The paper documents the physical link between weather and AQI using a city-season-level specification (equation 5, p. 7):

$$AQI_{jt,s} = b_0 + b_1 \times \text{Wind Speed}_{jt,s} + b_2 \times \text{Rain Volume}_{jt,s} + B \times X_{jt} + \delta_j + \delta_t + \varepsilon_{jts} \tag{5}$$

where $AQI_{jt,s}$ is the average daily AQI in city $j$ in season $s$ of year $t$; city and year fixed effects are included; standard errors are clustered by city and year. The sample covers 2003-2018: all-city models have 1,699 city-season observations and northern-city models have 845. Wind speed and rain volume are both negative across heating and non-heating seasons (Table 3, p. 7), confirming that weather disperses particulates and lowers AQI.

Step 3: Instrument construction. Policy uncertainty $PU_{j,t-5:t}$ is defined as the standard deviation of the characteristics-adjusted residuals from equation (4) over a six-year rolling window ending at $t$. The first stage regresses $PU$ on six-year rolling standard deviations of windy days and rainy days (equation 6, p. 8):

$$PU_{j,t-5:t} = c_0 + c_1 \times \text{SD Windy Days}_{j,t-6:t-1} + c_2 \times \text{SD Rainy Days}_{j,t-6:t-1} + C \times X_{jt} + \delta_j + \delta_t + \varepsilon_{jt} \tag{6}$$

The fitted weather component alone forms the instrument (equation 7, p. 9):

$$\hat{PU}_{j,t-5:t} = \hat{c}_1 \times \text{SD Windy Days}_{j,t-6:t-1} + \hat{c}_2 \times \text{SD Rainy Days}_{j,t-6:t-1} \tag{7}$$

The first stage includes AQI and city and average-firm controls, city and year fixed effects, city and year clustered standard errors, and 1,340 city-year observations over 2009-2019 (Table 4 Model 2, p. 8). Estimates are $\hat{c}_1 = 0.01^{***}$ (t = 3.27), $\hat{c}_2 = 0.16^{***}$ (t = 3.71); the first-stage F-statistic is 14.5. One-SD weather variability raises measured policy uncertainty by 0.228 SD. The estimation builds on instrumental-variables and panel-regression primitives.

Empirical specifications

City-level green R&D (R1, R2). The second stage regresses next-year city-level green R&D on instrumented policy uncertainty and average subsidy (equation 8, p. 9):

$$\text{Green R\&D}_{j,t+1} = d_0 + d_1 \times \hat{PU}_{j,t-5:t} + d_2 \times \text{Avg Subsidy}_{j,t-5:t} + D \times X_{jt} + \delta_j + \delta_t + \eta_{j,t+1} \tag{8}$$

where $\text{Green R\&D}_{j,t+1}$ is log aggregate green R&D of city $j$‘s listed firms, $\hat{PU}$ is the weather-instrumented policy uncertainty (equation 7), $\text{Avg Subsidy}_{j,t-5:t}$ is the six-year moving average of the characteristics-adjusted subsidy, and $X_{jt}$ includes AQI and city and firm-level controls. The 2009-2019 city-year sample has 1,340 observations; city and year fixed effects are included, with standard errors clustered by city and year. Headline estimates: $d_1 = -2.23^{***}$ (t = -2.64) and $d_2 = 0.34^{***}$ (t = 2.76), Table 4 Model 5.

The one-standard-deviation translation: $(0.01 \times 25.3 + 0.16 \times 1.95) \times (-2.23) / 9.54 = -0.132$ SD in green R&D per SD in weather variability (footnote 18, p. 9).

Firm-level heterogeneity (R4, R5). The city-level instrument enters a firm-level regression interacted with firm-type indicators (equation 9, p. 10):

$$\text{Green R\&D}_{ic,j,t+1} = e_0 + e_1 \times \hat{PU}_{j,t-5:t} + e_2 \times \text{Firm Type}_{ic,jt} \times \hat{PU}_{j,t-1} + e_3 \times \text{Avg Subsidy}_{j,t-5:t} + E \times X_{ic,jt} + \delta_i + \delta_t + \eta_{ic,j,t+1} \tag{9}$$

with firm and year fixed effects; the table reports robust t-statistics. The 2009-2019 sample has 26,189 firm-year observations. Firm type indicators include green tech (CSRC industry N77), metropolitan area, large size, state ownership, subsidy reliance (above-median ratio of environmental subsidy to total assets), cross-city registration, and industry affiliation (manufacturing, chemical, metal, public facility). The green-tech and subsidy-reliant interactions are the most significant (Table 6, pp. 10-11).

Green R&D employment (R3). Equation (8) replaces the dependent variable with $\log(1 + \text{total R\&D and technical employees in city } j)$. Result: PU = -1.07*** (t = -3.41), Table 8 Model 1.

Financial-constraint interactions (R14). Table 7 extends equation (9) across the firm sample by adding pairwise and triple interactions among instrumented uncertainty, green-tech status, and one financing proxy at a time (Table 7, p. 12). The specifications include the corresponding lower-order terms, city and firm controls, and firm and year fixed effects. The constraints are over-indebtedness or either interest-coverage ratio, estimated in separate columns; robust t-statistics are reported. Table 7 samples range from 24,068 to 26,189 firm-year observations over 2009-2019. The over-indebtedness interaction is negative, while the interest-coverage interactions are positive, consistent with greater financial pressure amplifying the negative uncertainty effect.

Placebos (R6). Equation (8) with three alternative dependent variables: total R&D minus green R&D, city capital expenditure, and city capital expenditure of real estate firms (Table 5 Models 6-8). None shows a significant effect of $\hat{PU}$, supporting the exclusion restriction.

Robustness. Table 5 reports eight robustness checks including: excluding city-years with zero green R&D, two-year average AQI, weather-adjusted AQI, subsidy starting from 2007, and a subsample from 2013 onward. The main result is stable across all variations (PU coefficients ranging from -2.22 to -5.14, all significant at 5% or better except in Model 6 which is a placebo). Baker, Bloom and Davis (2016) style policy uncertainty indices serve as a conceptual foil; the paper distinguishes its weather-based channel from those political and fiscal sources.

The estimation sample is 2009-2019, pre-Covid. City-year panel: 1,340 observations (up to 352 cities). Firm-year panel: 26,189 observations (3,168 firms).

Datasets used

Dataset	Role in paper	Wiki page
CSMAR (China Stock Market & Accounting Research Database)	Firm-level green R&D (from financial statement footnotes), environmental subsidy receipts, stock return	No page yet
CNRDS (China Research Data Services)	City-level GDP, GDP growth, consumption, population growth; firm characteristics digitized from China Statistical Yearbook	No page yet
WIND Financial Terminal	Firm-level turnover, ROA, leverage, size, cash holdings, capex, profit margin	No page yet
Ministry of Ecology and Environment (MEPC)	Annual average AQI for major cities from the national monitoring network	No page yet
China Meteorological Administration (CMA)	Daily city-level wind speed (m/s) and rain volume (mm) used to construct Windy Days, Rainy Days, and their rolling standard deviations	No page yet
EPSnet	City-level industrial Waste Gas Treatment expenditure; used to confirm the AQI-subsidy relationship independently (Table 2 Models 5-6)	No page yet

Sample: weather and subsidy data back to 2003 (needed for six-year rolling windows); main regressions: 2009-2019 (pre-Covid). 352 Chinese cities; 3,168 listed firms; 1,340 city-year observations in primary city-level regressions; 26,189 firm-year observations in firm-level regressions.

When to read the full paper

Read the original if you are:

• studying how uncertainty about government policies, not just their level, can undermine their intended effects on investment;
• building a weather-based IV design for policy uncertainty in settings where subsidies are tied to observable environmental indicators;
• interested in the institutional mechanics of Chinese environmental policy and AQI-linked subsidy allocation;
• examining heterogeneous investment responses to policy uncertainty across firm types (green-tech, subsidy reliance, financial constraints);
• researching the real-side effects of climate policy uncertainty for the growing green finance literature.

The online appendix (available at the DOI) contains additional institutional background, variable definitions, and further robustness checks.

Attribution and rights

Source: peer-reviewed, Journal of Financial Economics 175 (2026) 104189. This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced. The paper is paywalled; only extract-only redistribution is permitted.

Wang, Mengyu, Jeffrey Wurgler, and Hong Zhang. “Policy uncertainty reduces green innovation.” Journal of Financial Economics 175 (2026) 104189. DOI: 10.1016/j.jfineco.2025.104189. © 2025 Published by Elsevier B.V.