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Poison or catalyst? How do energy saving targets matter for firm-level productivity in China

Business

Poison or catalyst? How do energy saving targets matter for firm-level productivity in China

P. Zhang, A. Zhang, et al.

This study conducted by Pan Zhang, Acheng Zhang, and Zitao Chen explores how energy-saving targets from China's Top-10000 Enterprises Program influence firm-level total factor productivity. Discover the intriguing inverted-U relationship between these targets and productivity, and how appropriate target setting can yield environmental and economic benefits.

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Playback language: English
Introduction
Climate change, driven largely by CO2 emissions from fossil fuel combustion, presents a significant global challenge. China, with its energy-intensive economy and substantial CO2 emissions (30.3% of global energy-related emissions in 2018), faces immense pressure to reduce its carbon footprint. To address this, China implemented the Top-10000 Enterprises Energy Conservation and Low Carbon Program in 2011, setting binding energy-saving targets for over 10,000 enterprises. The impact of these targets on firm-level productivity, specifically TFP, remains a subject of debate. Previous studies have yielded conflicting results, some suggesting positive impacts and others negative impacts on TFP. This research aims to clarify these discrepancies by analyzing the effect of these targets on TFP, exploring the mediating role of market share, and examining the heterogeneity of effects across different regions, enterprise types, and industries. The study’s importance lies in its potential to inform policy decisions regarding the optimal design and implementation of energy-saving targets to balance environmental protection with economic growth.
Literature Review
Existing literature examines two main types of environmental targets in China: those set for local governments (focused on energy intensity) and those set for enterprises (energy-saving targets). Research on government-level targets shows varying effects depending on factors like regional emissions and inter-governmental competition. Studies focusing on enterprise-level targets, specifically the Top-1000 and Top-10000 programs, have explored aspects such as target allocation, implementation challenges, and the impact on firm profitability and exports. However, the literature concerning the impact on TFP remains limited and contradictory. Two studies examining the Top-1000 program offered contrasting conclusions regarding the effect on TFP – one showing a positive impact, the other a negative impact. This lack of consensus highlights the need for a more comprehensive analysis, particularly considering the larger scale of the Top-10000 program.
Methodology
This study employs a panel data model using data from two sources: (1) enterprise-level energy-saving targets from the National Development and Reform Commission (NDRC)'s Top-10000 program and (2) the China Industrial Enterprise Database (CIED) for 2012-2013. The dataset, initially comprising 16,373 organizations, was refined to focus on 10,667 industrial enterprises after excluding non-industrial entities and addressing missing or inconsistent data. TFP was measured using the Levinsohn-Petrin (LP) method, with robustness checks using the Olley-Pakes (OP) and fixed-effects (FE) methods. The independent variable was the energy-saving target, adjusted by firm size to account for scale differences. The study employed two sets of regression models. The first set explored the relationship between the energy-saving targets and TFP, initially using a linear model and then introducing a quadratic term to capture a potential non-linear (inverted-U-shaped) relationship. The second set investigated the mediating role of market share using a mediation model. Control variables included return on assets (ROA), financial leverage, fixed assets, firm size, enterprise ownership (SOE vs. non-SOE), export status, and firm age. Additional robustness checks were performed through winsorizing the independent variable, using alternative TFP measurement methods, controlling for other contemporaneous policies (low-carbon city pilot and carbon emission trading system pilot), excluding samples from four municipalities, controlling for year-industry, year-province and industry-province fixed effects, and applying Causal Mediation Analysis (CMA). Heterogeneity analysis was conducted to examine the differences in target effects across eastern/non-eastern regions, enterprise types (SOEs vs. non-SOEs), and industries (mining, manufacturing, power, gas, and water production and supply).
Key Findings
The study found a statistically significant inverted-U-shaped relationship between energy-saving targets and TFP. The linear model showed a positive relationship, but incorporating the quadratic term revealed that the positive effect diminishes at higher target intensities, indicating a turning point where increased targets lead to decreased TFP. The turning point was estimated to be around 37.2 (target intensity). Market share was found to mediate the relationship between energy-saving targets and TFP. Higher targets increased market share, which in turn positively influenced TFP. The results were robust across multiple robustness checks, including alternative TFP measures and controls for other policies. Heterogeneity analysis revealed significant differences across regions, enterprise types, and industries. Eastern region enterprises showed an earlier turning point (19.6) than non-eastern enterprises (49.2), suggesting a faster adoption of efficient technologies in the east. SOEs showed a later turning point (69) than non-SOEs (30.8), likely due to governmental support and less pressure for immediate productivity gains. Manufacturing industries exhibited greater resilience to higher targets due to their substantial energy-saving potential.
Discussion
The findings address the research question by showing that energy-saving targets can positively influence firm-level TFP, but only up to a certain intensity. Beyond this threshold, the cost of compliance may outweigh the benefits, leading to a decline in productivity. The mediating role of market share highlights a crucial pathway through which targets affect TFP. By incentivizing firms to improve efficiency and gain a competitive edge, targets can lead to market share expansion and ultimately higher profitability and productivity. The heterogeneity findings underscore the importance of tailoring target setting to specific regional contexts, enterprise types, and industries, considering their varying capacities and responsiveness to policy interventions. This study clarifies the conflicting results of previous research by demonstrating that the impact of energy-saving targets is not simply positive or negative but rather contingent on the target intensity and various contextual factors.
Conclusion
This study contributes to the literature by demonstrating the non-linear relationship between energy-saving targets and TFP, highlighting the mediating role of market share and identifying substantial heterogeneity in the effects across various contexts. The findings suggest that a nuanced approach to target setting is needed, adjusting target intensity based on regional context, enterprise type, and industry characteristics. Future research could explore the long-term effects of these targets, investigate the role of technological innovation in mediating the relationship between targets and TFP, and examine the interaction between energy-saving targets and other policy instruments.
Limitations
The study's main limitations include the use of data from only two years (2012-2013) preventing a thorough analysis of the long-term impact of the program. The limited timeframe may also mask the gradual effects of the policy on TFP. Data limitations also prevented a detailed exploration of the role of technological innovation in shaping the relationship between energy-saving targets and TFP. Future studies should consider using longer time series data and incorporating measures of technological innovation to obtain a more comprehensive understanding.
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