1 Introduction
2 Literature review
3 Data and methodology
3.1 Data and variables
Table 1 Variable definitions |
Variables | Definition |
---|---|
INV | Corporate investment; calculated as capital expenditure scaled by total assets in a previous year |
CPU | Climate policy uncertainty index; calculated by Gavriilidis [7] |
Leverage | Calculated as total company debt/shareholder’ s equity |
TQ | Tobin’s Q; calculated as market value scaled by total assets |
GDP | Real GDP growth |
Sales | Sales growth rate; calculated as the natural logarithm of sales in year t minus the natural logarithm of sales in year t-1 |
Asset | Asset growth rate; calculated as the natural logarithm of total firm assets in year t minus the natural logarithm of total firm assets in year t-1 |
This table shows the definition and calculation methods for all variables in this paper. “INV” is the interpreted variable, and “CPU” is the primary explanatory variable of our study. Additionally, other variables are the control variables to reduce the bias of our empirical models. |
Table 2 Descriptive statistics |
Variables | Obs | Mean | SD | Min | Median | Max |
---|---|---|---|---|---|---|
INV | 1664 | 0.07 | 0.05 | 0.0001619 | 0.0542005 | 0.2485481 |
CPU | 1664 | 2.04 | 0.15 | 1.773317 | 2.023386 | 2.300853 |
Leverage | 1664 | 0.57 | 0.26 | 0.065325 | 0.570164 | 2.25272 |
TQ | 1664 | 1.66 | 2.39 | 0.1467915 | 0.9307284 | 17.0436 |
GDP | 1664 | 0.08 | 0.02 | 0.061 | 0.078 | 0.142 |
Sale | 1664 | 0.05 | 0.17 | −0.6353185 | 0.0286031 | 0.8160951 |
Asset | 1664 | 0.12 | 0.25 | −0.346988 | 0.0709115 | 1.554026 |
This table shows the descriptive statistics of the variables of this study. “INV” is the interpreted variable, and “CPU” is the primary explanatory variable of our study. Additionally, other variables are the control variables to reduce the bias of our empirical models. |
3.2 Methods
3.2.1 Panel unit root test
3.2.2 Regression model
4 Results and discussion
4.1 Panel unit root test and multicollinearity test
Table 3 IPS panel unit root test and variance inflation factor test |
Variable | Statistic | p-value | VIF |
---|---|---|---|
INV | −9.2594 | 0.0000 | |
CPU | −2.3002 | 0.0107 | 1.0576 |
Leverage | −5.3371 | 0.0000 | 1.0898 |
TQ | −4.2299 | 0.0000 | 1.1417 |
GDP | 17.4159 | 0.0000 | 1.1440 |
Sale | −11.9619 | 0.0000 | 1.0485 |
Asset | −12.4421 | 0.0000 | 1.0503 |
This table shows the unit root test results. When the value of P is less than 0.05, the unit root does not exist, and the panel sequence is stable. It can be seen that all the data series in this paper are stationary. From the test results of the VIF, there is no significant multicollinearity among the variables. |
4.2 Regression analysis
4.2.1 Impacts of climate policy uncertainty
Table 4 Estimation results of the panel model |
VARIABLES | (1) | (2) | (3) |
---|---|---|---|
Overall | Industry I | Industry II | |
L.CPU | −0.0486*** | − 0.0019 | − 0.0423** |
(0.0173) | (0.0113) | (0.0195) | |
L.Leverage | −0.1072** | −0.0158 | − 0.0987** |
(0.0480) | (0.0399) | (0.0474) | |
L.TQ | 0.0002 | −0.0076 | 0.0065* |
(0.0056) | (0.0050) | (0.0033) | |
L.GDP | 0.2030*** | 0.3083** | 0.0214 |
(0.0493) | (0.1491) | (0.2898) | |
L.Sales | 0.1047 | −0.0050 | 0.0569*** |
(0.0505) | (0.0535) | (0.0189) | |
L.Asset | 0.1162 | −8.88e-05 | −0.00728 |
(0.0257) | (0.00707) | (0.00647) | |
Constant | 0.0147** | 0.0193* | −0.00213 |
(0.00736) | (0.00988) | (0.0106) | |
Observations | 1536 | 936 | 600 |
Number of firms | 128 | 78 | 50 |
This table shows the empirical outcomes of the panel estimation for the effect of climate policy uncertainty on firm investment in Chinese energy-related companies. Industry I denotes the production and supply of electricity, heat, gas, and water. Industry II is the mining industry, which takes national standards for the industry classification by China Securities Regulatory Commission (CSRC) as reference. Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1. |
4.2.2 Asymmetric impacts of climate policy uncertainty under different market conditions
Table 5 Estimation results of the dynamic threshold panel model |
VARIABLES | (1) | (2) | (3) |
---|---|---|---|
Overall | Industry I | Industry II | |
L.INV | 0.489*** | 0.502*** | 0.418*** |
(0.0277) | (0.0154) | (0.0236) | |
L.CPU-below threshold | − 0.0113* | 0.0256*** | − 0.0365*** |
(0.00658) | (0.00445) | (0.00633) | |
L.CPU-above threshold | −0.00930 | 0.0214*** | −0.0328*** |
(0.00579) | (0.00395) | (0.00575) | |
L.Leverage | −0.0548*** | −0.0379*** | − 0.0534*** |
(0.00886) | (0.00618) | (0.00500) | |
L.TQ | −0.00174 | − 0.00533*** | 0.00146** |
(0.00111) | (0.00122) | (0.000648) | |
L.GDP | 0.431*** | 0.325*** | 0.406*** |
(0.0584) | (0.0434) | (0.0606) | |
L.Sales | 0.0175* | −0.0612*** | 0.0564*** |
(0.00920) | (0.0139) | (0.00464) | |
L.Asset | 0.00465 | 0.0331*** | 0.00206 |
(0.00763) | (0.00563) | (0.00342) | |
Constant | 0.0465*** | −0.0156* | 0.0874*** |
(0.0135) | (0.00851) | (0.0148) | |
Threshold point | 2.023386 | 1.92843 | 2.023386 |
Observations | 1536 | 936 | 600 |
Number of firms | 128 | 78 | 50 |
This table reports the empirical results of the dynamic threshold model for the effect of climate policy uncertainty on firm investment in Chinese energy-related companies. Industry I denotes the production and supply of electricity, heat, gas, and water. Industry II is the mining industry, which takes national standards for the industry classification by China Securities Regulatory Commission (CSRC) as reference. Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1. |