*4.1. Unit Root Test and Co-Integration Test*

In the panel models, non-stationary sequences data cause the problem of spurious regression, which further leads to errors in estimating the results. To avoid this circumstance, we adopted five commonly-used unit root tests [47–51] to examine the stationarity of the data. Note, to save space, we only report the results of the unit-root test and co-integration test for the main regression (full sample); all the other regressions also passed these two tests, and the results are available from the authors upon request. Table 4 displays the results of the unit root test. All the variables were stationary sequences. However, as some of the variables were not significant in certain unit root tests, we examined the stationarity of the first-order difference of the variables, and the results indicated that all the unit root tests were significant at the 1% level, which implied that all the variables were at least integrated at an order of one. We further investigated the co-integration relationship among the panel data series using three co-integration tests [52–54]. The results in Table 5 show that the null hypotheses of "no co-integration" were rejected by all three tests, which implied that the co-integration relationship did exist; therefore, we continued our research by establishing the panel data model.


**Table 4.** Panel unit root test.


**Table 4.** *Cont.*

Notes: CE denotes carbon emission. FD1 denotes financial development 1. TRADE denotes trade openness. URBAN denotes urbanization. POP denotes population growth. IND denotes industrial structure. D. denotes the first-order difference of each variable. LLC denotes Levin-Lin-Chu test. IPS denotes Im–Pesaran–Shin test. HT denotes Harris–Tzavalis test. The values in parentheses are the *p*-values. \*\*\*, \*\*, and \* indicate significance at 1%, 5%, and 10% levels, respectively.



Notes: Values in parentheses are the *p*-values. \*\*\*, \*\*, and \* indicate significance at 1%, 5%, and 10% levels, respectively.

#### *4.2. Result of Full Sample Regression*

Table 6 presents the full sample of the empirical results of the effect of financial development on carbon emissions with stepwise regressions. For all the regressions, the first-order serial correlation tests were significant at the 1% level, and the second-order serial correlation tests and Hansen tests were not significant. These misspecification tests proved the appropriateness of the GMM specification. The results of the regression showed that financial development had a positive effect on carbon emissions, as the coefficients were positive and significant at the 1% level. This implied that financial development could increase carbon emissions from a global perspective. The stepwise regressions indicated that our main conclusion was not affected by the change in the control variables.




**Table 6.** *Cont.*

Note: L. denotes the first-order lag term of variables. AR (1) denotes the first-order autocorrelation estimator. AR (2) denotes the second-order autocorrelation estimator. Values in parentheses are standard errors. For AR (1), AR (2), and the Hansen test, the values in parentheses are the *p*-values. \*\*\*, \*\*, and \* indicate significance at 1%, 5%, and 10% levels, respectively. These notes are the same for the following tables.

According to the theoretical analysis, the influence of financial development on carbon emissions is uncertain. Some scholars [8,10,11] consider that financial development could fund the innovative activities of enterprises and environmentally friendly projects, which improve productivity and decrease the use of energy, thereby reducing carbon emissions. This can be called the "negative effect" of financial development on carbon emissions. However, other scholars [12–14] report that the development of the financial sector could stimulate the demand for energy consumption and the expansion of production scale, which increase carbon emissions. This can be called the "positive effect" of financial development on carbon emissions. Overall, the total impact is determined by the relative size of the negative and positive effects [18,55]. The empirical results indicated that the positive effect exceeds the negative effect in our sample and occupied the dominant position. Therefore, this result showed that on a worldwide level, the effect of financial development was more a promotion than reduction of carbon emissions. This conclusion is consistent with the work of Al-Mulali et al. [21], Bekhet et al. [23], and Lu [24].
