Emissions Reduction Target Plan and Export Product Quality: Evidence from China’s 11th Five-Year Plan
1. Introduction
After more than 40 years of reform and opening-up, China has created an export miracle relying on its immense demographic dividend as well as cheap resources. However, in addition to integrating into the global value chain with low-value-added products, China has also triggered a growing number of environmental problems. Thus, China’s manufacturing sectors face a double whammy, internal environmental constraints and external green barriers to exports, and the upgrade to export quality is expected soon. In 2006, the State Council issued the “11th Five-Year Energy Conservation and Emission Reduction Work Plan”, which proposed major pollutant mitigation targets, and linked them to local officials’ promotions. This policy not only influences firms’ export scales but also, more importantly, export quality.
Regarding China’s 11th Five-Year Plan as a quasi-natural experiment, we construct a difference-in-difference-in-difference (DDD) model to analyze the effect of its emissions reduction plan on export product quality. We begin by comparing export product quality for firms located in provinces with higher emissions reduction targets to those with lower emissions reduction targets, before and after the 11th Five-Year Plan. Then, we compare these estimates for firms in polluting or dirty industries. The main contributions of this study are as follows:
2. Policy Background of the Emissions Reduction Target Policy
To further curb environmental pollution, more binding policies to control pollution discharges were proposed in the 11th Five-Year Plan. This new pollution-mitigation policy led to the implementation of a variety of environmental regulations in all regions of China (rather than the TCZ areas only). The proposed environmental regulations were more prominent, highlighted by the following two components. Firstly, the State Council set a long-term emissions reduction goal for each provincial and local government, and the Central government set a total reduction target of 10% of major emissions at the national level and announced a mandatory target responsibility system to curb pollution discharges.
The second component of the environmental regulations was that the Central government linked the major pollutant reduction targets to local officials’ promotions. The National Development and Reform Commission issued a document entitled “Decision on Implementing Scientific Development Concepts and Strengthening Environmental Protection” on 3 December 2005. It declared environmental protection an important criterion for cadre assessment and official promotion. This promoted a shift in local officials’ assessment criteria from an economic performance index to an environmental–economic indicator. Environmental performance became an essential component of cadre selection and appointment. This marked the first time the Central government enforced the environmental protection “accountability system” and the “one-veto negation system”, making environmental performance an essential component of cadre selection and appointment. The State Council then promulgated “the Comprehensive Work Plan for Energy Conservation and Emissions Reduction”. Additionally, the National Environmental Protection Agency, the National Statistics Bureau, and the National Development and Reform Commission were required to disclose discharge data every 6 months and conduct annual inspections and assessments beginning in 2006. In 2008, an interim assessment of the results of the pollution reduction targets was conducted, and the final assessments were conducted in 2010.
Statistics showed that 27 provinces had established performance appraisal in environmental management and had incorporated environmental protection into their assessment systems for economic and social development. Moreover, 21 provinces had incorporated environmental protection into their assessment systems for cadre achievements, first establishing a chief cadre responsibility system in environmental protection. Emissions reduction goals had produced impressive results. Total SO2 and COD were decreased by 14.4% and 12.5%, respectively, during the 11th Five-Year Plan, and all provinces reached their emissions reduction targets.
3. Empirical Specifications and Data
3.1. Model Specifications
Combining the variation in the emissions reduction targets across provinces and export product quality data from before and after the 11th Five-Year Plan, we conducted a quasi-difference-in-difference model to capture how emissions reduction policy affects firms’ export behavior.
where Qualityfict is the export quality for a product in industry i exported by firm f to destination country c in year t, and Targetp represents the pollution reduction target for province p in which firm f is located. This can be further divided into SO2 reduction targets (target1) and COD reduction targets (target2). Postt is a dummy variable which equals 1 for 2006–2010, or 0 otherwise, denotes the SO2 emissions intensity in industry i, while denotes the COD emissions intensity in industry i.
Province–year fixed effects (), industry–year fixed effects (), province–industry fixed effects (), and firm–destination fixed effects () were controlled. To address potential heteroskedasticity and serial correlation, we clustered the standard errors at the province–industry level.
3.2. Data Source
We constructed data samples from three official statistical databases. The pollution reduction target data were from “The Documents of Objectives and Responsibilities in Reducing the Total Amount of Major Pollutants During the 11th Five-Year Plan”, which was issued by the China State Council in 2006.
The export data were collected from the China Customs Import and Export Database, which provides a record of all Chinese trade transactions. The initial customs data were aggregated to the 6-digit international Harmonized Commodity Description and Coding System (the Harmonized System, or HS for short) level because there were major adjustments for Chinese HS 8-digit codes, before and after 2002.
The data on emissions intensity for each four-digit industry were collected from China’s Environmental Statistics (CES) Database which discloses firm-level emissions monitored by the Ministry of Ecology and Environment of China. The average emissions intensity for each four-digit industry was calculated according to data on firm emissions discharges from that industry.
Based on the firms’ location and year, we matched the pollution reduction target data with the China Customs Import and Export Database. Thus, we matched the 6-digit HS classification with the 4-digit CIC codes using information from the National Bureau of Statistics of China. In this way, we can identify which industry an export product is from.
3.3. Variables
where xfhct and pfict denote the demand and price of the product in industry i exported by firm f to destination country c in year t, respectively. φi denotes the product fixed effect, and denotes the country–year fixed effect. εfhct is the error term. Then, the estimated quality is
where the elasticity of substitution (σ) was drawn from estimates of [32].
The main independent variables were the SO2 emissions reduction target (lntarget1) and the COD emissions target (lntargets2) for each province in natural logarithm form. For provinces with a pollution reduction target of zero, we set lntarget to zero.
4. Main Results
4.1. Baseline Analysis
4.2. The Parallel Trend Assumption
where is a series of estimates from 2002 to 2010, and Postt is the dummy variable indicating the given year t. The estimated coefficients are also presented in Appendix A. The coefficients of the triple interactions are insignificant from 2002 to 2005, suggesting a reduction in export quality in polluting industries proportional to the importance of the emissions reduction plans.
4.3. Robustness Checks
We conduct a series of robustness checks to confirm our estimation results, including alternative measures and specifications, and controlling for concurrent events, sample selection bias, and the endogeneity problem.
4.3.1. Alternative Measures and Specifications
4.3.2. Controlling Concurrent Events
We also control for other events during the 11th Five-Year Plan that may simultaneously affect firm performance. Three events stand out: the global financial crisis, the Beijing Olympic Games, and the energy-conservation plan promulgated by the Five-Year-Plan.
4.3.3. Sample Selection Bias
4.3.4. Dealing with Endogeneity
4.4. Heterogeneity Analysis
5. Further Analysis
5.1. Potential Mechanism Analysis
5.1.1. Policy Mechanisms
To test innovation compensation effects, we consider the number of green invention patent applications as a proxy for green innovation (we collected the number and IPC classification of firm-level patent applications from the State Intellectual Property Office (SIPO), and then use the IPC classification number of the green patent in the “International Green Patent Classification List” launched by the World Intellectual Property Organization (WIPO) to identify the number of green patents applied by firms each year) and introduce it in logarithm form. We also use total factor productivity (TFP) as an alternative measure for firm innovation. The estimated results reported in Columns (4)–(5) show that the coefficients for the triple interactions are positive and significant (p < 5%), suggesting that the implementation of emissions reduction targets leads to an increase in firms’ green innovation as well as their TFP.
5.1.2. Institutional Mechanisms
5.1.3. Summary in Mechanisms
In conclusion, our mechanisms consist of two negative effects and one positive effect which is the innovation compensation effect. It shows that Porter’s hypothesis still remains in our research. More importantly, we find that local officials who face promotion pressure are incentivized to raise emissions reduction goals, which may lead to a decrease in export product quality. In addition, increasing emissions abatement costs result in an increase in compliance costs, and it declines the export quality. Under the constraint of the pollution reduction target, the firm’s export behavior is affected by both positive incentives and negative limitations. Judging by the baseline and series of robustness analysis, we can corroborate that the negative mechanisms surpass the innovation compensation effect, which leads to a decline in export product quality in general.
5.2. Mitigating the Negative Effects of Emissions Reduction Plans
5.2.1. Core Product vs. Non-Core Products
5.2.2. First-Mover Advantage vs. Late-Mover Advantage
We also define a set of time ranges for the order through which a firm enters into a given market (Order). The significant negative coefficient Order in Column (4) implies that the later the firms enter foreign markets, the lower the export product quality. However, the coefficients for the triple interaction terms among emissions reduction policy and access order (order) are not significant. This indicates that the late-mover advantage has little effect on the relationship between emissions reduction plans and export quality.
6. Conclusions and Policy Implications
Along with the acceleration of industrialization in China, environmental pollution, which has received extensive public attention, is even worse than originally thought. Thus, environmental governance is now a prominent problem concerning the national economy and residents’ livelihood. Administrative regulation is the most direct and effective method for protecting the environment. At present, the objectives of environmental regulations include both pollution reduction and energy conservation. Thus, it is crucial to examine the trade effects of environmental regulations. Although researchers have tested the effects of environmental regulations on firm exports, research on the relationship between environmental regulations and product-level export quality has been lacking.
We have investigated environmental regulation’s effects on export product quality. To control for the potential endogeneity of environmental regulations, we chose the Chinese government’s emissions reduction plan proposed in the 11th Five-Year Plan as a quasi-experiment. By using a uniquely detailed dataset comprising Chinese export data at the firm, product, and destination country levels from 2000 to 2010, we find that in more pollution-intensive industries, stricter emissions reduction targets reduce export quality. This finding is robust to a series of robustness checks and the consideration of endogeneity problems, as well as sample selection bias. Furthermore, this negative effect is stronger if the firm is located in Western regions or the firm is state-owned. Our extended results indicate that product switching contributes to resource allocation within firms towards their core products, and mitigates the new environmental policy’s effects on export product quality. Additionally, firms can capitalize on their first-mover advantage and past experience to respond to emissions reduction plans while maintaining export quality.
Our findings have important policy implications for realizing environmental protection while simultaneously increasing trade. The relationship between the Central and local governments should be clarified, and a more transparent and practical responsibility mechanism should be established in environmental assessments. Tying emissions quotas to performance in local officials’ evaluation systems can promote emissions reduction. Moreover, our mechanism shows that stricter emissions reduction targets increase the adoption of emissions abatement facilities. In this way, local government could assist firms in lowering the costs by raising government subsidies or procurement. In addition, the Central and local governments should reinforce the effects of innovation offsets through policy support and avoid a one-size-fits-all policy approach in environmental regulation. Further preferential policies supplemented by policies that incentivize innovation should be implemented in under-developed regions and state-owned firms. Finally, governments should formulate favorable policies to guide firms to reallocate their resources into core products by product switching or rebuilding their first-mover advantage.