How Does Carbon Emissions Efficiency Affect OFDI? Evidence from Chinese Listed Companies

Abstract

With the in-depth promotion of the “double carbon” strategy, the effectiveness of the green and low-carbon transition is not only fundamental to breaking the environmental shackles of domestic economic development, but is also an inevitable choice for Chinese enterprises to participate in international economic cooperation in the context of global climate change. However, the relationship between green low-carbon transition effects and outward foreign direct investment (OFDI) has not been adequately studied, and the transmission mechanism is not yet clear. Based on the above research gaps, this study made an empirical analysis on how carbon emissions efficiency affects companies’ OFDI using the OFDI data of China’s A-share-listed companies and matching carbon emissions efficiency data with the cities where listed companies are located, from 2007 to 2019. This study found that carbon emissions efficiency increases the possibility of OFDI, and carbon emissions efficiency significantly expanded the scale of OFDI through reducing financing costs and improving technological innovation, and the regression results are all significantly positive at the 1% level. We used ventilation coefficients as the instrumental variable, and the 2SLS results showed that this correlation is still robust. The heterogeneity analysis found that the role of carbon efficiency in promoting OFDI is more prominent for SOEs, large companies, clean companies and companies in competitive markets. In addition, financial development can positively moderate the influence of carbon emissions efficiency on OFDI, and carbon emissions efficiency deepens the embeddedness of the investment market compared to the degree of diversification of the corporate OFDI market. This research deepens the theoretical study on the factors of China’s OFDI, and provides insights for the government to coordinate carbon emissions efficiency and OFDI growth to achieve sustainable development. This study proposes continuing to promote clean production enterprises to shape their own sustainable development advantages, continuing to optimise the market environment and talent development environment, grasping the financing policy and technical support of the two important means, and accelerating the internationalisation of self-owned brands. These are the urgent priorities in driving Chinese enterprises to ‘go global’.

1. Introduction

The report of the 20th National Congress of the Communist Party of China, which proposed “unswervingly expanding openness”, once again shows that by “going global”, Chinese companies can strengthen the link between domestic and international markets and resources, realise a more open dual circulation of domestic and foreign markets, and enhance their competitive advantage in fierce international cooperation. Therefore, going global is not only the need to foster the new development paradigm, but is also an essential part of a higher-level open economy. However, in recent years, global climate change caused by greenhouse gas emissions such as CO₂ has become the biggest non-traditional security challenge for the community of a shared future for mankind [1]. Countries are beginning to tighten their scrutiny of foreign investment, requiring multinationals to engage in green investment, sustainable development and other socially responsible practices [2]. Chinese enterprises face many uncertainties and challenges in outward foreign direct investment (OFDI). To cope with global climate change and ecological environmental deterioration, China is actively exploring a green and low-carbon path to development. As a broad and profound economic and social transformation, the green and low-carbon transition will inevitably have a significant impact on many areas such as investment, production, distribution and consumption at the macro level. At the micro level, it brings a series of challenges to enterprises such as changing business models, adapting production methods, transforming old and new dynamics and innovating in green technologies. Thus, can the green, low-carbon shift be translated into new competitiveness for companies in OFDI? In targeting the “carbon peaking and carbon neutrality goals”, what new opportunities can be found for Chinese OFDI in the continued efforts to go green and low-carbon? What new advantages can be tapped for OFDI? These are the topics of interest in this study.

Previous studies about the impact of environmental regulation on OFDI fuel a long debate. Some studies hold that environmental regulation may force enterprises to innovate, thus improving productivity and promoting OFDI [3]; other studies found environmental regulation reduces enterprises’ willingness to operate across borders. Moreover, environmental regulation may not be significantly correlated with OFDI. All of the above are previous studies on the impact of environmental regulation on OFDI based on different sample selections; they focused on the role of government-initiated top-down environmental regulation on OFDI, while neglecting the impact of the consequences of environmental policies on OFDI. In fact, after years of environmental protection practices by the Chinese government, the quality of the environment has improved significantly. As an important indicator for assessing environmental performance, studying the impacts of carbon emissions efficiency on OFDI is an extremely meaningful research topic.

In this study, we measured the carbon emissions efficiency of Chinese cities and combined the firm-level data; then, we employed the probit model to analyse the relationship between the carbon emissions efficiency and the enterprises’ OFDI decisions. In addition, we utilised the two-way fixed effects model further investigate the influence of carbon emissions efficiency on the enterprises’ OFDI scale and its underlying mechanism, and explored the influence from the perspective of different property rights, pollution intensities, enterprise sizes and market competitive level. Furthermore, we discussed the moderating effect of financial development level, and analysed the impact of carbon emissions efficiency from the perspective of depth and breadth. We found that carbon emissions efficiency is positively related to the enterprises’ OFDI decisions, and it expands the enterprises’ OFDI scale through two channels: reducing corporate financing costs and improving corporate technological innovation.

2. Literature Review

We focus on the links between carbon emissions efficiency and OFDI, because the debate on the linkage between environmental issues and OFDI is a long-standing hot topic. In the early years of research, many scholars mainly studied the environmental effects of OFDI, both on home and host countries; however, the scholars’ research conclusions are quite different. From the perspective of host countries, there are two opposing views on the environmental effects of OFDI: one is the “pollution heaven” hypothesis, in which developed countries transfer highly polluting and sunset industries through OFDI, then cause environmental pollution in host countries [4]; and the other is the “pollution halo” hypothesis, in which enterprises spread green technology through OFDI, contributing to energy efficiency and emissions reduction in the host countries [5]. From the perspective of home countries, by investing in other countries, firms have the potential to learn cleaner production technologies and gain reverse technology spillovers [6], which reduces environmental pollution [7] and carbon emissions [8,9] in the home countries. However, OFDI can also increase environmental pollution and raise carbon emissions in home countries by increasing the size of the domestic economy.

In the context of increasingly tightening global resources and environmental regulations, scholars have begun to explore the impact of environmental regulation on OFDI, and have come up with contrasting research results. Firstly, environmental regulation has an incentive effect on OFDI. In raising environmental standards and imposing higher pollution control costs to enterprises, enterprises are more inclined to adopt OFDI to avoid the increased costs [10,11]. Strict environmental regulation also forces enterprises to innovate, and thus compensate for environmental costs and productivity gains; driven by innovation, this can promote OFDI. Secondly, formal environmental regulation has a significant negative effect on OFDI; it can promote industrial upgrading and service industry development to prevent domestic capital outflow [12]. Thirdly, there is a non-linear relationship between environmental regulation and OFDI. When the intensity of environmental regulation is low in the home country, it will promote OFDI through the “pollution port effect”, and when the stringency of environmental regulation exceeds a certain threshold, it will produce a “shutdown effect” that inhibits OFDI [13].

In summary, the existing literature on OFDI and environmental pollution provides a helpful reference for this study, but there are some research gaps, as follows: firstly, previous literature has mostly considered the environmental impact of OFDI, but the relationship between environmental issues and the economy is not one-way. The reverse study of the economic impact of environmental effectiveness needs to be improved. Therefore, this study examined the impact of the environment on OFDI. Secondly, studies of environmental impacts on OFDI that focus on the role of top-down government environmental regulation, and studies exploring the impact of the effectiveness the low-carbon transitions on OFDI in the context of “carbon peaking and carbon neutrality “goals, are still relatively rare. We know that environmental regulation is an influencing factor on OFDI, and the consequences of these implemented policies need to be monitored and evaluated for their effectiveness on an ongoing basis; thus, whether the effectiveness of their policies can become a new advantage for firms in OFDI is the research topic of this study. In the institutional context of China’s ecological environmental protection, energy conservation and emission reduction practices over the years, this research theoretically and empirically verified the economic consequences on OFDI by evaluating the emission reduction effects, in order to fill the gap.

3. Research Hypothesis

Under the new pattern of global ecological and environmental co-governance, corporate environmental performance as a component of ownership advantages has received widespread attention from countries around the world, and its impact on OFDI has become increasingly important [14]. For example, host countries set environmental thresholds to restrict investment by enterprises with poor environmental performance [15]. Improved carbon emissions efficiency conveys a signal of green production and development, showing obvious advantages: enterprises in regions with high carbon emissions efficiencies are more likely to gain environmental recognition from the host country for their green image. These enterprises can be downward-compatible with high carbon-emitting host countries, help host countries reduce their carbon emissions, improve host countries’ ecological environment, thus making it easier to carry out foreign investment activities. Through specific analysis, we believe that the impact of carbon emissions efficiency on OFDI can be summarised into two channels: financing costs and technological innovation.

3.1. The Financing Cost Effect

The financing problem is a major challenge for enterprises to “go global” [16]. OFDI is a long project cycle, requires a large amount of funds, and its income uncertainty requires enterprises to have a complete capital chain and sufficient cash flow. These adverse investment characteristics increase the external financing costs and difficulties of OFDI. Reducing financing costs and easing financing constraints can promote OFDI. Improved carbon emissions efficiency can alleviate financing constraints and reduce financing costs: first, low-carbon development has become an important standard of urban civilisation, and the improvement of environmental conditions reflects the improvement of urban civilisation, which has a long-term impact on promoting urban economic development; this enhances urban reputation and international status, and improves urban appearance and comprehensive environment [17]. These comprehensive and positive effects can provide a good business and financing environment for enterprises. The source of improved carbon emissions efficiency in cities is enterprises taking action to reduce carbon emissions, and in the process, companies signal to external information users that they are actively engaged in social responsibility. These can help enterprises enhance their reputations and build environmentally friendly images. Thereby, regulators will reduce the level of regulation on these enterprises, enabling them to save on current financing costs and hidden costs, effectively alleviating their current and future financing difficulties. Secondly, as the government and the public attach importance to green development, policies such as green investment and green credit have been introduced to provide credit support to environmentally friendly enterprises. Enterprise environmental performance can influence financial institutions’ judgment of an enterprise’s credit risk. Enterprises with high levels of environmental governance can alleviate green credit constraints, obtain green credit and green investment, and highlight their green reputation. Green reputation can spread the green competitive advantages of enterprises to the public, effectively improving enterprises’ financing capabilities and promoting enterprises’ development and growth [18], thus making enterprises more capable of OFDI.

3.2. The Technological Innovation Effect

Relevant research shows that enterprises need to have sufficient innovation capabilities to enter the international market and overcome liabilities of foreignness successfully in the host country [19]. Innovation, as an important source of competitive advantage for enterprises is the basic prerequisite, and effectively guarantees enterprises to enter the host country market and conduct business activities successfully [20]. Carbon emissions efficiency can improve the level of technological innovation of enterprises in the following two aspects: firstly, the level of air pollution and environmental quality in cities can have an impact on the flow of human capital. Improving urban carbon emissions efficiency is conducive to attracting high-level talents to settle in [21]. High-quality talents can provide innovative ideas and technological support for enterprises, promoting innovation and technological upgrading of enterprises, in order to enhance the long-term and sustainable competitiveness of enterprises. Secondly, improvements in urban carbon emissions efficiency can, to a certain extent, characterise the actual effect of green technology innovation activities, which is beneficial for cities to siphon off technological innovation enterprises from all over the world. In this process, cities absorb and introduce new inventions and technologies, strengthen the core of urban science and innovation, facilitate the formation of clusters of high-tech industries in the spatial pattern, and reduce the search cost and risk cost of green innovation for enterprises, effectively enhancing independent innovation capabilities. Improvements in technological innovation encourages OFDI [22].

4. Methodology Specifications and Data

4.1. Model Construction

Two aspects of corporate OFDI are of concern to this study: the propensity of enterprises to outward foreign direct investment and the scale of enterprises to outward foreign direct investment. A probit model was constructed, with the binary dependent variable (Decision) of whether the enterprise conducts OFDI to discuss the impact of carbon emissions efficiency on the enterprise’s OFDI decision-making, using the number of investment enterprises as the dependent variable (OFDI), and using a two-way fixed effects model to explore the impact of carbon emissions efficiency on the scale of OFDI. Based on the previous analysis and taking into account the specific situation of Chinese enterprises, this research established the following econometric models for empirical testing:

$$P(Decision=1|X_{it})={\alpha }_0+{\alpha }_{1}ef{f}_{ct}+{\alpha }_2{X}_{it}+{\mu }_t+{\mu }_{ind}+{\epsilon }_{it}$$

(1)

$$OFD{I}_{\mathrm{it}}={\beta }_0+{\beta }_{1}ef{f}_{ct}+{\beta }_2{X}_{it}+{\mu }_t+{\mu }_{ind}+{\epsilon }_{it}$$

(2)

where Decision denotes OFDI decisions of enterprises, OFDI_it denotes the OFDI scale of enterprise i in year t. eff_ct denotes carbon emissions efficiency of city c in year t. X_it is a matrix of control variables. μ_t controls for firm-fixed effects over time, μ_ind controls industry effects, and ε_it is a random disturbance term.

4.2. Variable Description

4.2.1. Dependent Variables

Outward foreign direct investment Decision (OFDI_D): if the company has OFDI in the year, the value of this variable is 1; otherwise it is 0.

Outward foreign direct investment Scale (OFDI_S): expressed by the number of all overseas affiliates of a multinational enterprise per year.

4.2.2. Explained Variable

Carbon emissions efficiency (Eff_CO2): data envelopment analysis (DEA) and its derived measurement models are the main methods for measuring carbon emissions efficiency. Since each decision-making unit will choose the input and output weight that is most conducive to maximising its own efficiency, the cross-efficiency evaluation method uses the average of the cross efficiency between decision-making units to characterise the efficiency of a single entity, overcoming the shortcomings of traditional self-evaluation DEA models that do not consider competition between decision-making units [23]. Therefore, this study selected the cross-efficiency evaluation method to measure carbon emissions efficiency. Labour, capital and energy were used as input factors, carbon emissions as undesired output and regional GDP as desired output. Labour input was measured by the total number of employees; capital, according to the method commonly used in domestic and international literature, this paper used the perpetual inventory method to estimate the capital stock, and to eliminate the effect of the price factor using the fixed asset price index for the 2000 base period. Total energy consumption was measured by converting to standard coal on the basis of urban labour, total natural gas supply, total liquefied petroleum gas (LPG) supply, total electricity consumption of the whole society and total heat supply. The carbon emissions data were obtained by inverting the simulation of night-time lighting; the brighter the lights at night, the higher the level of economic development of the city and the higher the energy consumption.

4.2.3. Control Variables

Control variables were considered from both the internal characteristics and external influencing factors of the enterprise. The internal characteristics include the enterprise’s age (Age), shareholding ratio of the largest shareholder (Fsr), asset liability ratio (Lev), equity ratio (Eur), Tobin Q value (Tobin Q), and fixed asset ratio (FAR). The external factors mainly include industrial structure (Ind: the ratio of the secondary industry output value to total output value), human capital (Hum), and the intensity of financial science and technology expenditure (Gov: the proportion of government financial science and education expenditure to GDP).

4.3. Data Source

This study selected Chinese A-share-listed enterprises from 2007 to 2019 as the research samples to investigate the impact of carbon emissions efficiency on OFDI. In the sample selection process, this study performed the following steps on the data: ① to avoid the influence of the enterprise’s operating status, we excluded sample enterprises that are marked as ST, *ST, and those went bankrupt; ② we excluded overseas companies that registered places concentrated in the Cayman Islands, the British Virgin Islands and Bermuda, because such investments are mainly motivated by tax avoidance; ③ we excluded the sample of enterprises whose relevant variable data are missing or obviously do not comply with accounting standards. In the end, this study obtained 28,812 firm-year data from 3520 listed companies, which included 11,968 non-zero observations from 2136 companies. The enterprises’ financial information and overseas affiliated subsidiary information used in this research came from the CSMAR database. The list of outward foreign direct investment by listed companies was from the “Directory of Overseas Investment Enterprises (Institutions)” of the Ministry of Commerce, and the urban economic data were from the China Urban Statistical Yearbook.

5. Findings and Discussion

5.1. Baseline Regression Results

First, we examined the impact of carbon emissions efficiency on enterprises’ OFDI decisions. The estimated results are shown in columns (1)–(3) of

Table 1. Baseline regression results.

	(1)	(2)	(3)	(4)	(5)	(6)
	Decision: Probit Model			Scale: Fixed Effect Model
Variables	OFDI_D	OFDI_D	OFDI_D	OFDI_S	OFDI_S	OFDI_S
Eff_CO2	0.774 ***	0.449 ***	0.424 ***	0.362 ***	0.136 ***	0.143 ***
	(0.02)	(0.02)	(0.02)	(7.79)	(2.83)	(2.87)
Age		−0.058 ***	−0.054 ***		−0.008	−0.001
		(0.01)	(0.01)		(−0.47)	(−0.06)
Fsr		−0.005	−0.019		0.101 **	0.084 **
		(0.02)	(0.02)		(2.51)	(2.10)
Lev		0.346 ***	0.355 ***		0.735 ***	0.737 ***
		(0.01)	(0.01)		(21.68)	(21.72)
Eur		−0.002 ***	−0.003 ***		−0.003 ***	−0.003 ***
		(0.00)	(0.00)		(−2.84)	(−2.86)
Tobin Q		−0.014 ***	−0.014 ***		−0.011 ***	−0.011 ***
		(0.00)	(0.00)		(−3.81)	(−3.69)
FAR		−0.227 ***	−0.211 ***		−0.272 ***	−0.251 ***
		(0.02)	(0.02)		(−5.38)	(−4.94)
Ind			−0.282 ***			−0.177 **
			(0.03)			(−2.32)
Hum			−0.014 ***			−0.007 **
			(0.00)			(−2.48)
Gov			0.384			2.318 ***
			(0.37)			(2.78)
Industry fixed effect	N	Y	Y	N	Y	Y
Year fixed effect	N	Y	Y	N	Y	Y
Constant				1.065 ***	0.926 ***	0.924 ***
				(43.12)	(15.32)	(10.81)
Observations	28,812	28,739	28,739	11,968	11,967	11,967
R-squared				0.005	0.109	0.113

Note: T-values are shown in parentheses; ** 5% significant level; *** 1% significant level.

, reporting the marginal effects of the probit model. Column (1) shows the results that only include the core explanatory variable. Column (2) adds company level control variables, and simultaneously fixed the industry and year. Column (3) adds city level control variables. The results show that the carbon emissions efficiencies are significantly positive at the 1% significance level, preliminarily confirming the positive impact of carbon emissions efficiency on OFDI possibilities. In addition to investment decisions, columns (4) to (6) use the fixed effect model to explore the impact of carbon emissions efficiency on OFDI scaling. Column (4) shows the results of the core explanatory variable, and column (5) and (6) shows the results of adding company and city level control variables and controlling industry and year fixed effects. The core explanatory variables are significantly positive at the 1% significance level. In summary, the baseline regression results indicate that when carbon emissions efficiency is higher, the possibility and the scale of enterprises’ OFDI will significantly increase. Improving the level of urban carbon emissions efficiency will improve the city’s reputation, enhance the reputation of enterprises for green development, shape new competitive advantages in the international market, and promote the enterprises’ OFDI decisions and expand the scale of OFDI.

5.2. Robustness Test

(1) The core explained variable was replaced. The carbon emissions efficiency was measured using the unexpected output excess efficiency SBM model to replace the cross-efficiency model. The regression results are shown in column (1) of

Table 2. Results of robustness test and 2SLS regression.

	(1)	(2)	(3)	(4)	(5)
	Replace	Winsorise	Remove	First	Second
Variables	OFDI_S	OFDI_S	OFDI_S	OFDI_S	VC
Eff_CO2		0.148 ***	0.167 ***		1.245 **
		(2.98)	(3.25)		(2.42)
Eff_CO22	0.067 ***
	(2.69)
VC				0.013 ***
				(10.77)
Controls	Y	Y	Y	Y	Y
Industry fixed effect	Y	Y	Y	Y	Y
Year fixed effect	Y	Y	Y	Y	Y
Constant	0.945 ***	0.916 ***	0.755 ***	0.597 ***	0.068
	(11.36)	(10.18)	(7.74)	(37.46)	(0.20)
Observations	11,967	11,967	9236	11,968	11,968
R-squared	0.113	0.117	0.124	0.226	0.078

Note: T-values are shown in parentheses; ** 5% significant level; *** 1% significant level.

. The regression coefficient of the core variables did not significantly change compared to the baseline regression.

(2) Winsorisation was performed. In order to eliminate the influence of outliers of the variables on the reliability of the regression results, a 1% tail reduction (winsorise) was applied to the left and right ends of the continuous variables, and then regressed again, as shown in column (2) of Table 2. The reliability of the baseline regression results is once again verified by the fact that carbon emissions efficiency can still significantly promote OFDI.

(3) The special samples were removed. Considering that four municipalities directly under the Central Government have significant geographical, economic, and political advantages, their economic development level and degree of openness are among the top in the country, and the number of multinational enterprises was also significantly different from other cities. Excluding the sample data from Beijing, Tianjin, Shanghai and Chongqing, the regression was performed again. The results are shown in column (3) of Table 2, and the core explained variable is still significant at the 1% level.

5.3. Endogenous Problem Analysis

The possible causes of endogenous problems in this study are as follows: firstly, reverse causal problems. Improving carbon emissions efficiency will promote OFDI. Similarly, when enterprises conduct OFDI activities, they will affect regional carbon emissions through pollution transfer, technology spillovers, and other channels. Secondly, there may be a problem of lacking variables. OFDI can be affected by various factors. Although this research controlled some factors at the enterprise and city levels, other influencing factors may still have been omitted from the model.

In order to alleviate endogenous factors, we needed to find an instrument variable that is highly related to carbon emissions efficiency, but does not directly affect OFDI. Previous studies have shown that the higher the regional wind speed, the easier it is to disperse pollutants into the air, thus reducing the degree of pollution; the higher the air flow coefficient, the stronger the air fluidity and the faster the dissipation of atmospheric pollutants. This study used the ventilation coefficients (VC) as the instrumental variable [24]. The ventilation coefficients are equal to the value of the 10-meter wind speed multiplied the boundary layer height. The regression results are shown in Table 2 (4) and (5). The regression coefficient of VC is significantly positive at the level of 1%, indicating that carbon emissions efficiency is positively correlated with VC, satisfying the correlation assumption of the instrumental variable. The results of the second stage show that the regression coefficient of carbon emissions efficiency is significantly positive after adding the instrumental variable, indicating that the improvement in carbon emissions efficiency significantly improves the OFDI scale. The above tests went some way to alleviating the endogenous factors.

5.4. Mechanism Analysis

Based on the previous theoretical mechanism analysis, this study designed the following model to further examine the mechanism and path of the impact of carbon emissions efficiency on OFDI from two channels: financing cost and technological innovation.

$$medi{a}_{it}={\delta }_0+a_{1}ef{f}_{ct}+{\delta }_{it}{X}_{it}+u_t+{\mu }_{ind}+{\epsilon }_{it}$$

(3)

where media_it denotes the mediated variables, including financing costs and technological innovation. eff_ct denotes carbon emissions efficiency of city c in year t. X_it is a matrix of control variables. μ_t controls for firm-fixed effects over time, μ_ind controls industry effects, and ε_it is a random disturbance term.

(1) Financing costs. The financing costs directly reflect whether the enterprise is facing difficulties and high financing costs, and is one of the key obstacles for enterprises to OFDI. To test whether financing costs are a potential channel for carbon emissions efficiency to affect OFDI, this study used the ratio of corporate financial expenses to total liabilities to measure financing costs. In addition, the financing constraint SA index was also used as a substitute variable for financing costs. According to Ju et al. (2013) [25], the SA index is negative, and the higher the absolute value, the more financially constrained the enterprise. Thus, this study took an absolute value for the SA index. The mechanism testing results of financing costs shown in columns (1) and (2) of

Table 3. Results of mechanism analysis.

	(1)	(2)	(3)	(4)
Variables	Financing Cost	SA	Green Patent	Green Utility Model Patent
Eff_CO2	−0.009 *	−0.025 **	0.116 *	0.122 **
	(−1.88)	(−2.25)	(1.82)	(2.19)
Controls	Y	Y	Y	Y
Industry fixed effect	Y	Y	Y	Y
Year fixed effect	Y	Y	Y	Y
Constant	−0.065 ***	2.111 ***	−0.057	−0.069
	(−7.79)	(112.18)	(−0.52)	(−0.72)
Observations	11,967	11,967	11,967	11,967
R-squared	0.159	0.763	0.188	0.212

Note: T-values are shown in parentheses; * 10% significant level; ** 5% significant level; *** 1% significant level.

. In column (1), there is a significant negative correlation between carbon emissions efficiency and financing costs. Improving carbon emissions efficiency can significantly reduce financing costs for enterprises. Column (2) shows the regression result of carbon emissions efficiency on financing constraints, which is significantly negative at the 5% level. Carbon emissions performance could alleviate corporate financial constraints.

Since investors pay more attention to the environmental issues and green attributes of the enterprise when making investment decisions, improvements in carbon emissions efficiency means a reduction in environmental pollution and an improvement in air quality, which meets the public’s demand for environmental protection and sustainable development. It establishes a green image for the enterprise, improves its financing channels, brings financial advantages to it, thereby reducing its financing costs for internal development and external investment, and further promotes its growth of investment scale.

(2) Technological innovation. Innovation is of great significance for enterprises to enhance their competition in products and services. Only when enterprises have a competitive advantage can they smoothly enter the international market and overcome the weaknesses of overseas operations. To test whether technological innovation is a potential channel for carbon emissions efficiency to affect OFDI, this study used the logarithm of the number of green invention patent applications and the number of green practical patent applications of enterprises as a measure of the level of technological innovation. The mechanism test results of technological innovation are shown in columns (3) and (4) of Table 3. Whether it is the number of green invention patent applications or the number of green practical patent applications, the carbon emissions efficiency is significantly positive, and the improvement in carbon emissions efficiency significantly improves the level of technological innovation.

Relying on improvements in urban carbon emissions efficiency, this can attract a group of high-quality talents with public awareness to provide sufficient human capital and innovation resources for the green innovation of enterprises, promote their improvement of technological innovation, and enhance their international competitiveness in foreign direct investment. Therefore, improving the level of technological innovation in enterprises is another important influencing mechanism for carbon emissions efficiency to promote OFDI.

5.5. Heterogeneity Analysis

5.5.1. Differences in the Nature of the Enterprise

This study divided all samples into two groups: state-owned enterprises and non-state enterprises. The regression results are shown in columns (1) and (2) of

Table 4. Results of heterogeneity analysis.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Variables	OFDI_S	OFDI_S	OFDI_S	OFDI_S	OFDI_S	OFDI_S	OFDI_S	OFDI_S
Eff_CO2	−0.032	0.660 ***	0.175 ***	0.028	0.042	0.243 ***	0.139 ***	0.228
	(−0.57)	(6.15)	(3.14)	(0.25)	(0.69)	(3.45)	(2.67)	(1.30)
Controls	Y	Y	Y	Y	Y	Y	Y	Y
Industry fixed effect	Y	Y	Y	Y	Y	Y	Y	Y
Year fixed effect	Y	Y	Y	Y	Y	Y	Y	Y
Constant	0.906 ***	1.061 ***	0.951 ***	0.767 ***	1.114 ***	1.120 ***	0.879 ***	0.967 ***
	(9.17)	(5.72)	(9.96)	(3.97)	(10.60)	(8.94)	(9.70)	(3.51)
Observations	8386	3575	9742	2225	4809	7155	10,425	1541
R-squared	0.116	0.189	0.117	0.101	0.113	0.132	0.110	0.171

Note: T-values are shown in parentheses; *** 1% significant level.

. The coefficient of the core explanatory variable in the non-state-owned enterprises is not significant, while the carbon emissions efficiency coefficient in the state-owned enterprises is significantly positive at the 1% confidence level. The investment promotion effect of carbon emissions efficiency in state-owned enterprises is more obvious, and the logic behind it lies in the fact that state-owned enterprises regard environmental responsibility performance as their strategic goal, while private enterprises lack the strategic goal of environmental responsibility performance; thus, the environmental performance of state-owned enterprises is generally higher than that of private enterprises. Moreover, compared to state-owned enterprises, private enterprises receive less policy support [26], face greater financial constraints, and have more difficulties promoting OFDI. Therefore, state-owned enterprises have greater advantages in promoting the scale of OFDI.

5.5.2. Difference in Pollution Intensity

The sample enterprises were divided into clean industry enterprises and pollution intensive industry enterprises according to the degree of industry pollution intensity. The regression results are shown in columns (3) and (4) of Table 4. Under the influence of improved carbon emissions efficiency, enterprises in clean industries have a significantly increased scale of OFDI, while carbon emissions efficiency has no significant impact on OFDI in pollution-intensive industries. The reason is that the green and clean industry market has a high investment value and good development prospects. This information will guide investment into the market, making it easier to obtain green credit support, easing the financing constraints of enterprises in the clean industry, and promoting OFDI. Moreover, sufficient capital investment can promote the technological innovation of enterprises in such industries and cultivate the technological advantages of OFDI. Therefore, the environmental benefits of improving carbon emissions efficiency can promote OFDI in clean industries more significantly.

5.5.3. Differences in Enterprise Size

The sample was divided into large-scale enterprises and small and medium-sized enterprises. The regression results are shown in columns (5) and (6) of Table 4, and we found that large-scale enterprises have significant impact. The reason is that the large-scale enterprises are mostly in the growth and maturity stages, while small-scale enterprises are mostly in the start-up stage. Compared to small-scale enterprises, large-scale enterprises are more mature in both their operational behaviour and technical level. Moreover, the larger the enterprise size, the more cost advantages it has, the more sufficient funds it has, and the richer its human resources are, which in turn has greater advantages in increasing the scale of OFDI.

5.5.4. Differences in Market Competition

Enterprises in different levels of market competition perform differently in terms of operational risks, resource competition and other aspects. Therefore, the promoting effect of carbon emissions efficiency on OFDI may vary depending on market competition. This study constructed the HHI index, and divided the sample into high market competition and low market competition based on the median. The regression results are shown in columns (7) and (8) of Table 4. When the market competition is high, the impact of carbon emissions efficiency on OFDI is positively significant. In a fiercely competitive markets, enterprises face higher operational risks and more intense resource competition. Improving their carbon emissions efficiency can reduce the operational risks caused by environmental risks. At the same time, high carbon emissions efficiency can also serve as a green advantage for enterprises to win more external resources, solve financial difficulties, and thereby promote OFDI.

6. Further Analysis

6.1. The Regulatory Role of Financial Development Level

Due to the impact of carbon emissions efficiency on OFDI through easing financing constraints and reducing financing costs, the development of financial markets, as an important venue for enterprise financing, affects the effectiveness of enterprise financing, thereby affecting OFDI. A well-developed financial market in a country can create a good external financing environment for enterprises, which is conducive to boosting OFDI. However, a low level of financial development will inhibit the scale of OFDI. Based on a baseline regression, this research further introduced the interaction between carbon emissions efficiency and financial development level to analyse the regulatory effect of financial development level. The financial development level is measured by the proportion of loan balances from financial institutions to GDP. The regression result is shown in column (1) of

Table 5. Results of heterogeneity analysis.

	(1)	(2)	(3)
VARIABLES	OFDI_S	OFDI_D	OFDI_W
Eff_CO2	0.101 **	0.111 ***	0.023
	(1.99)	(4.82)	(0.65)
Fin	0.017 **
	(2.27)
Eff_CO2 × Fin	0.135 ***
	(3.37)
Controls	Y	Y	Y
Industry fixed effect	Y	Y	Y
Year fixed effect	Y	Y	Y
Constant	0.796 ***	0.799 ***	0.904 ***
	(20.22)	(13.18)	(9.22)
Observations	11,967	11,967	11,967
R-squared	0.135	0.110	0.114

Note: T-values are shown in parentheses; ** 5% significant level; *** 1% significant level.

. The interaction coefficient between carbon emissions efficiency and financial development is significantly positive, so financial development level positively regulates the promotion effect of carbon emissions efficiency on OFDI. We should orderly promote the reform of financial marketisation, build a multi-level financial support system, improve the efficiency of financial resource allocation, actively develop green credit, and “escort” outward foreign direct investment.

6.2. Dual Marginal Analysis of Foreign Direct Investment

The scale of OFDI can be further subdivided into the breadth and depth of OFDI. The breadth of OFDI reflects the degree of diversification of enterprises’ foreign direct investment, which is expressed by the number of host countries where enterprises have OFDI in one year. The depth of OFDI reflects the embedding degree of enterprises’ foreign direct investment, which is expressed by the average number of affiliated subsidiaries of a single host country in one year. Based on the dual perspective of investment depth and breadth, the impact of carbon emissions efficiency on OFDI was systematically explored. The regression results are shown in columns (2) and (3) of Table 5. Improvement in carbon emissions efficiency significantly enhances the depth of OFDI and failures to expand the breadth of OFDI. The possible reason is that the comparative advantage of improving carbon emissions efficiency lies in establishing a responsible green image for enterprises. Whether an enterprise can expand its existing investment in host countries and explore emerging markets is not limited to the green competitive advantage brought by improving its carbon emissions efficiency, but more depends on the enterprise’s own scale, and financial and technological advantages. However, improvements in enterprises’ carbon emissions performance can help them embed into the host country market, which means that responsible international investment can further promote the deepening of foreign investment cooperation. Enterprises consciously apply the concept of green development into their business activities, actively assume social responsibility, and enhance their competitiveness in global competition.

7. Conclusions and Policy Recommendations

7.1. Conclusions

This study used carbon emissions efficiency data from prefecture-level cities in China and OFDI data from A-share-listed enterprises in China from 2007 to 2019 to explore the impact of urban carbon emissions efficiency on enterprises’ OFDI. The following conclusions are drawn: firstly, carbon emissions efficiency plays a positive role in promoting OFDI decision-making, and expands the scale of OFDI through two channels: reducing financing costs and improving the level of technological innovation. Through a series of robustness tests, the results were still found to be valid. Secondly, state-owned enterprises, clean enterprises, large enterprises, and enterprises in competitive markets better grasp the advantage of carbon emissions efficiency to promote the expansion of OFDI. Thirdly, a higher level of financial development can strengthen the promotion of carbon emissions efficiency on OFDI; compared to expanding the OFDI range, improving the carbon emissions efficiency strengthens the degree of enterprise embeddedness in the host country market.

7.2. Policy Recommendations

Firstly, we recommend the ongoing monitoring and evaluation of policies. By monitoring the outcomes of implemented environment policies, policymakers can determine whether the intended goals and objectives are being achieved. Evaluating the effectiveness of policies provides valuable insights into their impact on encouraging sustainable OFDI and reducing carbon emissions. By regularly assessing policy effectiveness and making necessary adjustments based on real-world outcomes, policymakers can enhance the impact of their initiatives, foster a more sustainable business environment, and contribute to the global transition towards a greener and more resilient economy.

Secondly, we recommend continuing to promote cleaner production in enterprises and shaping their own sustainable development advantages. Only by practising the concept of green development can foreign investment cooperation become an important support for improving the quality of the dual cycle, play a key role in open development, and win the initiative in international cooperation and competition. Enterprises should comply with the requirements of sustainable development strategies; focus on long-term, green and healthy development; focus on clean production; further tap their energy conservation and emissions reduction potential; create low-carbon and clean advantages; and enhance international competitiveness. At the same time, enterprises should continuously strive to improve their operational efficiency, reduce the loss of funds in the management process, effectively leverage the advantages of sustainable development to bring convenience to themselves, and effectively invest resources in OFDI.

Thirdly, we should firmly grasp the two important means of financing policy and technical support to help Chinese enterprises “go global”. The Chinese government should expand diversified financing channels actively; formulate personalised financing support policies; further improve the efficiency of the financial market; provide more adequate financial services for multinational enterprises; effectively address long-term financing needs during the development process of enterprises; reduce the inhibitory effect of financing constraints on OFDI; and stimulate the potential for foreign investment in enterprises. The Chinese government should also emphasise the important role of technological innovation in the form of national strategies, help enterprises build technological innovation platforms to disperse technological innovation risks, guide enterprises to increase R&D investment, and improve innovation levels, in order to enhance the core competitiveness of China’s OFDI and promote the “incremental improvement” of OFDI.

Fourthly, the sustainable development advantages of state-owned enterprises, clean enterprises, and large enterprises should be strengthened, and continuous improvement in brand value should be promoted. State-owned enterprises, clean enterprises, and large enterprises better grasp the investment advantages of carbon emissions efficiency to promote the scale expansion of OFDI. Those enterprises should seize the momentum, and the government should award appropriate policy preferences to these enterprises, promote enterprises to create a responsible global leading brand images for sustainable development, thus continuously promoting the construction of Chinese brands. Enterprises should tell the story of Chinese brands well, transform brand management advantages into enterprise competitive advantages, enhance international competitiveness, expand investment location diversification, optimise production internationalisation layouts, accelerate the upward climb of GVC, and enhance the position of enterprises in the international division of labour.

Fifthly, enterprises should continue to optimise the market environment and talent development environment. The government should give full play to the role of government functions, carry out appropriate and necessary interventions, and promote effective market competition. Strong market competition will stimulate enterprises’ sustained innovation and upgrading motivation, and promote multinational corporations to further improve their comprehensive competitiveness. Broadening local talent introduction channels, establishing and improving green channels for talent introduction, and giving appropriate policy preferences to high-level innovative talents in urgent need of introduction can create a good talent growth environment, cultivate talent advantages and stimulate innovation vitality. Optimising the business environment and talent development environment of enterprises promotes the orderly and free flow of various factors, expands the main body of foreign trade operations, actively benchmarks international high standard economic and trade rules, and creates new advantages in international cooperation and competition.