Dual-Credit Policy of New Energy Automobiles in China: Corporate Innovation Capability
Abstract
:1. Introduction
2. Literature Review and Hypothesis Development
2.1. Innovation Capability and Its Determinants
2.2. Policy Instruments and NEV Industry Development
2.3. Dual-Credit Policy and Its Impact on NEV Manufacturers
2.4. Research Hypotheses
3. Data, Variables, and Empirical Models
3.1. Data Sources and Sample Selection
3.2. Variable Definitions and Measurements
3.2.1. Dependent Variable
3.2.2. Independent Variables
3.2.3. Mediating Variable
3.2.4. Control Variables
3.2.5. Dummy Variables
3.3. Empirical Models
- (a)
- The direct impact of Policyt on RDi.
- (b)
- The role of PolicyIntensityt and PolicyExposurei in affecting RDi.
- (c)
- The differential effect captured by the interaction term Policy × Treati.
- (d)
- The effect of other control variables such as ITRit, log (EmployeeCountit), ProfitMarginit, FirmAgeit, NEVFocusi, ROAit, and GRit on RDi.
- (a)
- The effect of policy on innovation capability, controlling for the treatment group and the interaction term between policy and treatment.
- (b)
- The effect of policy on R&D expenditure, capturing how Policyt and PolicyIntensityt influence RDi.
- (c)
- The effect of R&D expenditure on innovation capability, controlling for the policy.
3.3.1. Benchmark Regression Model
- t = 2013, 2014, …, 2022.
- β0 is the intercept.
- β1 is the coefficient for the lagged dependent variable, capturing the effect of the previous year’s innovation capability on the current year.
- β2 is the coefficient for the policy dummy variable, capturing the change in all companies after the policy implementation.
- β3 is the coefficient for PolicyIntensity, measuring the intensity of the Dual-Credit Policy over time.
- Β4 is the coefficient for the group dummy variable, capturing the inherent difference between NEV manufacturers and traditional manufacturers.
- Β5 is the coefficient for PolicyExposure, measuring the company’s exposure to the Dual-Credit Policy.
- β6 is the coefficient for the interaction term between Policy and Treat, capturing the net effect of the policy on the treatment group (NEV manufacturers).
- β7 is the coefficient for the interaction term between PolicyIntensity and Treat, capturing the differential impact of policy intensity on NEV manufacturers versus traditional manufacturers.
- β8 is the coefficient for the interaction term between PolicyIntensity and PolicyExposure, capturing the heterogeneous effect of policy intensity based on the level of policy exposure.
- β9 to β11 are the coefficients for the current and lagged R&D investment variables, capturing the effect of current and past R&D investments on innovation capability.
- β12 is the coefficient for the inventory turnover ratio, measuring its effect on innovation capability.
- β13 is the coefficient for the number of employees, capturing the effect of company size on innovation capability.
- β14 is the coefficient for profit margin, measuring the impact of profitability on innovation capability.
- β15 is the coefficient for the age of the company, capturing its effect on innovation capability.
- β16 is the coefficient for the company’s focus on NEV production, measuring its impact on innovation capability.
- β17 is the coefficient for return on assets, capturing its effect on innovation capability.
- β18 is the coefficient for growth rate, measuring its effect on innovation capability.
- β19 is the coefficient for the interaction term between PolicyIntensity and return on assets, exploring whether the effect of policy intensity varies with company profitability.
- β20 is the coefficient for the interaction term between PolicyIntensity and growth rate, investigating whether the effect of policy intensity differs for companies with varying growth opportunities.
- αi represents firm fixed effects, controlling for time-invariant unobserved heterogeneity across companies.
- δt represents time fixed effects (year dummy variables), controlling for common macroeconomic shocks and trends affecting all companies in a given year.
- εit is the random error term.
β6(Policyt × Treati) + β7 (PolicyIntensityt × Treati) + β8(PolicyIntensityt × PolicyExposurei) +
β9log (RDit) + β10log (RDi, t-1) + β11log (RDi, t-2) + β12ITRit + β13log (EmployeeCountit) + β14ProfitMarginit + β15FirmAgeit + β16NEVFocusi + β17ROAit + β18GRit+ αi + δt + εit
β9log (RDit) + β10log (RDi, t-1) + β11log (RDi, t-2) + β12ITRit + β13log (EmployeeCountit) + β14ProfitMarginit + β15FirmAgeit + β16NEVFocusi + β17ROAit + β18GRit + β19(PolicyIntensityt × ROAit) + β20(PolicyIntensityt × GRit) + αi + δt + εit
3.3.2. Mediation Regression Model
- β0 is the intercept.
- β1 captures the effect of the policy dummy variable on R&D expenditure.
- β2 measures the intensity of the policy over time.
- β3 accounts for the inherent differences between NEV manufacturers and traditional manufacturers.
- β4 measures the company’s exposure to the Dual-Credit Policy.
- β5 to β7 capture interaction effects between policy, policy intensity, treatment, and policy exposure.
- β8 to β14 are coefficients for control variables like inventory turnover ratio, employee count, profit margin, firm age, NEV focus, return on assets, and growth rate.
- β15 to β16 capture the interaction effects of policy intensity with return on assets and growth rate, respectively.
- αi and δt represent firm and time fixed effects, respectively.
- εit is the random error term.
+ γ4ITRit + γ5log (EmployeeCountit) + γ6ProfitMarginit + γ7FirmAgeit + γ8NEVFocusi + γ9ROAit + γ10GRit + αi + δt + εit
- γ0 is the intercept.
- γ1 to γ3 capture the effects of current and lagged R&D expenditure on innovation capability.
- γ4 to γ10 are coefficients for control variables like inventory turnover ratio, employee count, profit margin, firm age, NEV focus, return on assets, and growth rate.
- αi and δt represent firm and time fixed effects, respectively.
- εit is the random error term.
θ8log (RDit) + θ9log (RDi, t-1) + θ10log (RDi, t-2) + θ11ITRit + θ12log (EmployeeCountit) + θ13ProfitMarginit + θ14FirmAgeit + θ15NEVFocusi + θ16ROAit + θ17GRit + θ18(PolicyIntensityt×ROAit) + θ19(PolicyIntensityt × GRit) + αi + δt + εit
- θ0 is the intercept.
- θ1 to θ7 capture the direct effects of policy, policy intensity, treatment, policy exposure, and their interactions on innovation capability.
- θ8 to θ10 capture the effects of current and lagged R&D expenditure on innovation capability.
- θ11 to θ17 are coefficients for control variables like inventory turnover ratio, employee count, profit margin, firm age, NEV focus, return on assets, and growth rate.
- θ18 to θ19 capture the interaction effects of policy intensity with return on assets and growth rate, respectively.
- αi and δt represent firm and time fixed effects, respectively.
- εit is the random error term.
4. Empirical Results
4.1. Descriptive Statistics and Correlation Analysis
4.2. Baseline Regression Results
4.3. Mediation Analysis Results
4.4. Heterogeneity Analysis Results
5. Dynamic Effect Analysis
6. Threshold Effect Analysis
- Yit is the innovation output.
- xit is a vector of explanatory variables.
- qit is the threshold variable (PolicyIntensity).
- γ is the threshold value.
- eit is the indicator function.
- (a)
- Regime 1: PolicyIntensity ≤ 18.5
- (b)
- Regime 2: PolicyIntensity > 18.5
7. GMM Analysis (As an Additional Robustness Check and Methodological Extension)
7.1. Combined Time Series Model (Dynamic Panel GMM)
7.2. Post-Estimation Tests
8. Robustness Test
8.1. Parallel Trend Test
8.2. Placebo Test Results
9. Discussion, Implications, Limitations and Future Research
9.1. Discussion
9.2. Implications
9.3. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Year | Sales (Units) |
---|---|
2013 | 18,000 |
2014 | 75,000 |
2015 | 331,000 |
2016 | 507,000 |
2017 | 777,000 |
2018 | 1,256,000 |
2019 | 1,206,000 |
2020 | 1,367,000 |
2021 | 1,575,000 |
2022 | 6,887,000 |
Variable | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|
RD (100 million yuan) | 55.96 | 52.74 | 8.01 | 218.41 |
PatentCount (pieces) | 819.70 | 658.64 | 5 | 2345 |
ValidInventionPatent | 228.34 | 262.40 | 1 | 1072 |
PatentGrantRate (%) | 79.44 | 65.23 | 2.96 | 229.87 |
ROA (%) | 6.11 | 4.52 | −2.71 | 16.05 |
GR (%) | 13.53 | 21.37 | −28.82 | 96.20 |
ITR (%) | 10.68 | 4.31 | 3.06 | 19.76 |
PolicyIntensity (%) | 12.00 | 18.97 | 0 | 60 |
PolicyExposure (%) | 13.95 | 21.19 | 0 | 52.47 |
EmployeeCount (person) | 102,205 | 74,386 | 11,415 | 570,000 |
ProfitMargin (%) | 7.09 | 4.55 | −3.75 | 15.25 |
FirmAge (years) | 55.37 | 46.15 | 10 | 160 |
NEVFocus (%) | 13.75 | 22.01 | 0 | 99.06 |
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RD | 1.000 | ||||||||||||
Patent Count | 0.528 *** | 1.000 | |||||||||||
ValidInvention Patent | 0.486 *** | 0.784 *** | 1.000 | ||||||||||
Patent GrantRate | −0.103 | −0.215 ** | −0.182 * | 1.000 | |||||||||
ROA | −0.292 ** | −0.175 * | −0.087 | 0.106 | 1.000 | ||||||||
GR | 0.103 | 0.057 | 0.026 | 0.038 | 0.412 *** | 1.000 | |||||||
ITR | −0.253 ** | −0.286 ** | −0.315 ** | 0.094 | 0.528 *** | 0.176 * | 1.000 | ||||||
Policy Intensity | 0.314 *** | 0.215 ** | 0.043 | −0.132 | −0.324 ** | −0.075 | −0.312 ** | 1.000 | |||||
Policy Exposure | 0.037 | 0.076 | 0.112 | 0.053 | 0.185 * | −0.042 | −0.194 * | −0.086 | 1.000 | ||||
Employee Count | 0.784 *** | 0.426 *** | 0.372 *** | −0.087 | −0.315 ** | 0.128 | −0.283 ** | 0.286 ** | 0.015 | 1.000 | |||
Profit Margin | −0.315 ** | −0.186 * | −0.103 | 0.124 | 0.956 *** | 0.376 *** | 0.482 *** | −0.342 ** | 0.193 * | −0.342 ** | 1.000 | ||
Firm Age | 0.243 ** | 0.094 | 0.052 | −0.085 | −0.286 ** | −0.143 | 0.215 ** | 0.018 | −0.348 ** | 0.176 * | −0.275 ** | 1.000 | |
NEV Focus | 0.168 * | 0.286 ** | 0.324 *** | −0.042 | 0.076 | 0.143 | −0.253 ** | 0.324 *** | 0.684 *** | 0.103 | 0.085 | −0.426 *** | 1.000 |
Dependent Variable: IC | (1) | (2) |
---|---|---|
Policy | 328.15 ** | 265.37 ** |
(132.46) | (122.18) | |
Treat | 542.79 *** | 471.56 *** |
(148.23) | (140.75) | |
PolicyIntensity | 15.68 ** | 13.24 ** |
(6.34) | (5.87) | |
log(RD) | 295.63 *** | 272.41 *** |
(74.52) | (68.93) | |
PolicyIntensity × PolicyExposure | 0.87 ** | 0.76 ** |
(0.35) | (0.32) | |
PolicyIntensity × ROA | \ | 0.42 * |
\ | (0.23) | |
PolicyIntensity × GR | \ | 0.08 |
\ | (0.06) | |
ROA | \ | 12.76 |
\ | (11.45) | |
GR | \ | 2.35 |
\ | (3.21) | |
log(EmployeeCount) | \ | 185.92 ** |
\ | (79.84) | |
FirmAge | \ | −3.08 * |
\ | (1.75) | |
NEVFocus | \ | 7.95 ** |
\ | (3.28) | |
PolicyExposure | \ | 8.76 * |
\ | (5.12) | |
Constant | −2054.37 *** | −3087.25 *** |
(498.75) | (743.69) | |
Firm FE | Yes | Yes |
Year FE | Yes | Yes |
R2 | 0.482 | 0.561 |
Variable | Innovation Capability | |||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
Methods | DID | DID | DID | PSM-DID | PSM-DID | PSM-DID |
Policy | 312.78 ** | 284.53 ** | 265.94 ** | 303.45 ** | 276.82 ** | 259.37 ** |
(124.56) | (118.73) | (112.45) | (120.87) | (115.46) | (109.63) | |
Treat | 528.45 *** | 496.72 *** | 478.63 *** | 513.29 *** | 483.56 *** | 466.91 *** |
(147.62) | (140.35) | (134.87) | (143.18) | (136.74) | (131.53) | |
Policy × Treat | 345.67 *** | 317.82 *** | 295.36 *** | 335.78 *** | 309.45 *** | 288.72 *** |
(98.34) | (92.45) | (87.23) | (95.56) | (89.98) | (85.12) | |
PolicyIntensity | 14.23 ** | 12.87 ** | 15.45 ** | 13.86 ** | 12.53 ** | 15.02 ** |
(5.67) | (5.12) | (6.23) | (5.51) | (4.98) | (6.06) | |
log(RD) | \ | 287.56 *** | 271.84 *** | \ | 279.83 *** | 265.29 *** |
\ | (68.45) | (64.92) | \ | (66.57) | (63.24) | |
PolicyExposure | \ | 8.76 * | 9.34 * | \ | 8.52 * | 9.09 * |
\ | (4.56) | (4.87) | \ | (4.43) | (4.74) | |
ROA | \ | 11.23 | 10.56 | \ | 10.92 | 10.28 |
\ | (9.12) | (8.67) | \ | (8.87) | (8.43) | |
GR | \ | 2.45 | 2.31 | \ | 2.38 | 2.25 |
\ | (2.67) | (2.54) | \ | (2.59) | (2.47) | |
PolicyIntensity × PolicyExposure | \ | \ | 0.78 ** | \ | \ | 0.76 ** |
\ | \ | (0.31) | \ | \ | (0.30) | |
PolicyIntensity × ROA | \ | \ | 0.39 * | \ | \ | 0.38 * |
\ | \ | (0.21) | \ | \ | (0.20) | |
PolicyIntensity × GR | \ | \ | 0.07 | \ | \ | 0.07 |
\ | \ | (0.06) | \ | \ | (0.06) | |
Constant | −2134.67 *** | −3012.45 *** | −2945.78 *** | −2076.23 *** | −2931.56 *** | −2867.94 *** |
(487.23) | (652.78) | (639.45) | (473.56) | (634.87) | (622.13) | |
Control | No | Partial | Full | No | Partial | Full |
Firm FE | Yes | Yes | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
R2 | 0.446 | 0.512 | 0.537 | 0.437 | 0.503 | 0.528 |
Dependent Variable: IC | log(RD) | log(PatentCount) | log(PatentCount) |
---|---|---|---|
(1) | (2) | (3) | |
Policy | 0.287 *** | \ | 0.156 ** |
(0.084) | \ | (0.062) | |
PolicyIntensity | 0.015 *** | \ | 0.008 ** |
(0.004) | \ | (0.003) | |
Treat | 0.542 *** | \ | 0.312 *** |
(0.127) | \ | (0.094) | |
Policy × Treat | 0.318 *** | \ | 0.187 *** |
(0.093) | \ | (0.069) | |
PolicyIntensity × Treat | 0.012 ** | \ | 0.007 ** |
(0.005) | \ | (0.004) | |
log(RD) | \ | 0.534 *** | 0.428 *** |
\ | (0.087) | (0.079) | |
log(RD_t-1) | \ | 0.312 *** | 0.254 *** |
\ | (0.076) | (0.071) | |
log(RD_t-2) | \ | 0.187 ** | 0.143 ** |
\ | (0.065) | (0.061) | |
PolicyExposure | 0.009 * | \ | 0.005 * |
(0.005) | \ | (0.004) | |
PolicyIntensity × PolicyExposure | 0.002 ** | \ | 0.001 * |
(0.001) | \ | (0.0006) | |
ITR | −0.008 | −0.005 | −0.004 |
(0.006) | (0.005) | (0.005) | |
log (EmployeeCount) | 0.423 *** | 0.287 *** | 0.246 *** |
(0.098) | (0.076) | (0.072) | |
ProfitMargin | 0.015 ** | 0.009 * | 0.008 * |
(0.006) | (0.005) | (0.004) | |
FirmAge | −0.004 | −0.003 | −0.002 |
(0.003) | (0.002) | (0.002) | |
NEVFocus | 0.018 *** | 0.012 ** | 0.010 ** |
(0.005) | (0.004) | (0.004) | |
ROA | 0.021 ** | 0.014 * | 0.012 * |
(0.008) | (0.007) | (0.006) | |
GR | 0.003 | 0.002 | 0.002 |
(0.002) | (0.002) | (0.002) | |
PolicyIntensity × ROA | 0.001 * | \ | 0.0006 |
(0.0006) | \ | (0.0004) | |
PolicyIntensity × GR | 0.0002 | \ | 0.0001 |
(0.0002) | \ | (0.0001) | |
Constant | −3.876 *** | −2.543 *** | −2.187 *** |
(0.987) | (0.765) | (0.712) | |
Firm FE | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes |
R2 | 0.683 | 0.712 | 0.745 |
Variable | IC | RD | IC | IC | IC | RD | IC | IC |
---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
Methods | DID | DID | DID | DID | PSM-DID | PSM-DID | PSM-DID | PSM-DID |
Policy | 0.312 *** | 0.287 *** | \ | 0.156 ** | 0.303 ** | 0.279 *** | \ | 0.148 ** |
(0.084) | (0.084) | \ | (0.062) | (0.087) | (0.086) | \ | (0.064) | |
Treat | 0.528 *** | 0.542 *** | \ | 0.312 *** | 0.513 *** | 0.528 *** | \ | 0.302 *** |
(0.127) | (0.127) | \ | (0.094) | (0.131) | (0.130) | \ | (0.097) | |
Policy × Treat | 0.345 *** | 0.318 *** | \ | 0.187 *** | 0.335 *** | 0.309 *** | \ | 0.180 *** |
(0.093) | (0.093) | \ | (0.069) | (0.096) | (0.095) | \ | (0.071) | |
log(RD) | \ | \ | 0.534 *** | 0.428 *** | \ | \ | 0.521 *** | 0.416 *** |
\ | \ | (0.087) | (0.079) | \ | \ | (0.089) | (0.081) | |
log(RD_t-1) | \ | \ | 0.312 *** | 0.254 *** | \ | \ | 0.304 *** | 0.247 *** |
\ | \ | (0.076) | (0.071) | \ | \ | (0.078) | (0.073) | |
log(RD_t-2) | \ | \ | 0.187 ** | 0.143 ** | \ | \ | 0.182 ** | 0.139 ** |
\ | \ | (0.065) | (0.061) | \ | \ | (0.067) | (0.063) | |
Controls | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
R2 | 0.512 | 0.683 | 0.712 | 0.745 | 0.503 | 0.674 | 0.703 | 0.736 |
Method | Sobel Test | Bootstrapping | Proportion of Mediating Effect | Control Variable | Firm Fixed Effect | Year Fixed Effect |
---|---|---|---|---|---|---|
Index | Indirect Effect (a × b) | Indirect Effect (a × b) | Unit: % | Yes or no | ||
Sobel Test Statistic (z) | 95% Confidence Interval | |||||
p-value | Significance | |||||
DID | 0.136 *** | 0.136 *** | 74.5 | Yes | Yes | Yes |
3.092 | [0.0502, 0.2218] | |||||
0.00198 | Significance | |||||
PSM-DID | 0.129 *** | 0.129 *** | 73.6 | Yes | Yes | Yes |
2.957 | [0.0443, 0.2137] | |||||
0.00311 | Significance |
Variable | DID (Area = 1) | DID (Area = 0) | PSM-DID (Area = 1) | PSM-DID (Area = 0) |
---|---|---|---|---|
Policy × Treat | 423.67 ** | 215.33 | 411.22 ** | 209.45 |
(184.29) | (178.56) | (179.13) | (173.62) | |
p-value | 0.022 | 0.228 | 0.022 | 0.228 |
Constant | 578.33 ** | 312.67 | 562.11 ** | 304.29 |
(298.45) | (256.78) | (290.22) | (249.59) | |
p-value | 0.044 | 0.224 | 0.044 | 0.223 |
Control Variables | Yes | Yes | Yes | Yes |
Firm FE | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes |
R2 | 0.5123 | 0.4287 | 0.5082 | 0.4169 |
Variable | DID (ESG = 1) | DID (ESG = 0) | PSM-DID (ESG = 1) | PSM-DID (ESG = 0) |
---|---|---|---|---|
Policy × Treat | 415.75 ** | 223.25 | 403.28 ** | 216.55 |
(182.64) | (179.87) | (177.16) | (174.47) | |
p-value | 0.023 | 0.215 | 0.023 | 0.215 |
_cons | 592.50 * | 298.50 | 574.73 * | 289.55 |
(302.18) | (253.05) | (293.11) | (245.46) | |
p-value | 0.051 | 0.239 | 0.051 | 0.239 |
Control Variables | Yes | Yes | Yes | Yes |
Firm FE | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes |
R2 | 0.5235 | 0.4175 | 0.5078 | 0.4050 |
Variable | DID (SOE = 1) | DID (SOE = 0) | PSM-DID (SOE = 1) | PSM-DID (SOE = 0) |
---|---|---|---|---|
Policy × Treat | 357.50 * | 281.50 | 346.78 * | 273.06 |
(199.23) | (192.37) | (193.25) | (186.60) | |
p-value | 0.074 | 0.144 | 0.074 | 0.144 |
_cons | 521.75 * | 369.25 | 506.10 * | 358.17 |
(285.61) | (265.18) | (277.04) | (257.22) | |
p-value | 0.068 | 0.165 | 0.068 | 0.165 |
Control Variables | Yes | Yes | Yes | Yes |
Firm FE | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes |
R2 | 0.4912 | 0.4498 | 0.4765 | 0.4363 |
Year | Coefficient | Standard Error | p-Value |
---|---|---|---|
2018 | 0.089 * | 0.047 | 0.058 |
2019 | 0.152 ** | 0.061 | 0.013 |
2020 | 0.231 *** | 0.072 | 0.001 |
2021 | 0.305 *** | 0.083 | 0.000 |
2022 | 0.389 *** | 0.095 | 0.000 |
Control Variables | Yes | ||
Firm FE | Yes | ||
Year FE | Yes | ||
R2 | 0.7124 |
Regime | Coefficient | Std. Error | t-Statistic | p-Value |
---|---|---|---|---|
PolicyIntensity ≤ 18.5 | 0.103 ** | 0.042 | 2.405 | 0.015 |
Policyintensity > 18.5 | 0.189 *** | 0.043 | 3.714 | 0.000 |
Threshold Variable | PolicyIntensity | |||
Threshold Estimate | 18.5 | |||
95% Confidence Interval | [16.2, 20.2] |
Variable | Coefficient | Std. Error | z-Statistic | p-Value |
---|---|---|---|---|
log (Yi,t1) | 0.312 *** | 0.073 | 4.274 | 0.000 |
Policyt | 0.178 ** | 0.069 | 2.580 | 0.010 |
PolicyIntensityt | 0.026 *** | 0.008 | 3.250 | 0.001 |
Policyt × Treati | 0.203 *** | 0.061 | 3.328 | 0.001 |
PolicyIntensityt × Treati | 0.035 *** | 0.011 | 3.182 | 0.001 |
log (RDit) | 0.295 *** | 0.068 | 4.338 | 0.000 |
Variable | DID | PSM-DID | GMM |
---|---|---|---|
Policyt | 0.265 ** | 0.259 ** | 0.178 ** |
(0.122) | (0.110) | (0.069) | |
Policyt × Treati | 0.295 *** | 0.289 *** | 0.203 *** |
(0.087) | (0.085) | (0.061) | |
log(RDit) | 0.272 *** | 0.265 *** | 0.295 *** |
(0.069) | (0.063) | (0.068) |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Gary, J.; Zhao, P.; Bao, Z. Dual-Credit Policy of New Energy Automobiles in China: Corporate Innovation Capability. Sustainability 2024, 16, 7504. https://doi.org/10.3390/su16177504
Gary J, Zhao P, Bao Z. Dual-Credit Policy of New Energy Automobiles in China: Corporate Innovation Capability. Sustainability. 2024; 16(17):7504. https://doi.org/10.3390/su16177504
Chicago/Turabian StyleGary, Joston, Pengfei Zhao, and Zhihao Bao. 2024. "Dual-Credit Policy of New Energy Automobiles in China: Corporate Innovation Capability" Sustainability 16, no. 17: 7504. https://doi.org/10.3390/su16177504