Coping with Externally Imposed Energy Constraints: Competitiveness and Operational Impact of China's Top-1000 Energy-Consuming Enterprises Program.

AuthorXiao, Yuxian
PositionIron and steel, chemicals, power generation, petroleum and petrochemicals, construction materials, non-ferrous metals, coal mining, paper, and textiles

    With the growing global concern regarding climate change, policymakers in nations around the world have developed various programs to reduce their energy footprints. These programs could be market-based, taking the form of allocated permits such as the EU Emission Trading System; negotiated agreements, such as the Climate Change Agreement in the U.K. (Martin et al., 2014); or in a command-and-control form, such as China's Top-1000 program (Karplus et al., 2020). Understanding how enterprises will respond to these constraints is increasingly important for optimizing the design of such regulations. Among the three types of energy constraints, the command-and-control approach has received the least attention. This may be because the environmental regulatory regime is moving toward a more market-oriented approach, encouraged by the success of several grand cap-and-trade policy experiments. However, in many less developed countries, markets for environmental externalities are still at a nascent stage, and policy experiments are scarce. For instance, in China, the primary tool used to reduce energy use and environmental pollution is still the traditional command-and-control approach (NDRC, 2017a; Yin et al., 2019). As China and many other less market-oriented countries become more aggressive in combating carbon emission, there is a growing need to carefully study how enterprises may respond to the command-and-control based energy constraints, as well as the economic impacts of such constraints; this study is a response to this need.

    This study particularly contributes to a shared understanding of corporate responses to intensity standards. Even within the category of command and control approaches, regulations can be set to different standards, such as those for total emission, emission intensity, emission per unit of a specified input, or total output. Differences in these standards could lead to different resource-allocation incentives (Helfand, 1991). China's Top-1000 program uses the amount of energy saved (AES) target. Since the methods of calculating the achieved amount of energy saved critically hinge on how much the considered companies could lower their energy intensity, it is in essence an intensity standard. Firms can make different production decisions under this intensity-based standard: it may encourage participants to dilute energy use by increasing output (Fullerton and Heutel, 2010; Zhao et al., 2016); firms may achieve their energy-saving target by improving their technology (Zhu et al., 2018; Shao et al., 2019), or may divest energy-intensive parts of production to other firms (Feng et al., 2013). It is an empirical question which strategy would dominate. Regarding the current literature about China's Top-1000 program, Zhao et al. (2016) provide a detailed case study focusing on ten anonymous participants in Top-1000; Zhu et al. (2018) decompose the AES achievements for 580 enterprises in Top-1000 to detect the technological changes; and Karplus et al. (2020) investigate the potential data manipulation that exists in Top-1000's achievement evaluation. These studies reveal the unique command-and-control characteristics of the Top-1000 program and improve our understanding of the program's impact. However, these studies only focus on the Top-1000 enterprises and do not compare the program's participants with non-participants. Therefore, it is hard to tell whether the identified change is due to the impact of the program or a result of temporal shocks such as an economic downturn.

    In this paper, we use propensity score matching to identify pairs for each of the Top-1000 firms, then use a difference-in-differences approach to identify the program's resulting changes in profitability and operational choices. The firms covered by the Top-1000 program have clearly slowed down their production growth and increased their investment in fixed assets. These actions increase the production costs of covered firms, thus affecting their competitiveness. Our analysis is not only useful for understanding the corporate impacts of China's Top-1000 program, but also sheds lights on the incentives induced by intensity standards in general.

    In addition to elucidating how firms are expected to respond to externally imposed energy constrains, particularly in a command-and-control fashion, our paper also provides useful insights in the following two aspects: First, it contributes to a shared understanding of asymmetric regulations. A popular strategy in the battle against pollution is to regulate big firms. One reason for this is that big firms often cause most of the pollution and are therefore the source of the problem. For example, in the U.S., about 1250 entities produce more than 95% of its domestic CO2 emission, while in China, about 47% of the energy is consumed by around 1,000 firms (Bluestein, 2005; NDRC, 2006).Another reason is that small firms often cannot cope with the higher costs of environmental regulations, therefore, raise the issue of fairness and argue for moving slower with mitigation measures (Pashigian, 1984; Evans, 1986). Examples of strategies that target larger firms include the California Cap-and-Trade Program, which covers around 450 facilities that emit more than 25,000 tons per year, and China's Top-1000 Energy-Consuming Enterprises Program (hereafter referred to as the Top-1000 program), which only covers the Top-1000 firms in terms of energy consumption. The inherent incompleteness of asymmetric regulations raises concerns. The first issue is the negative economic impact on regulated firms or regions, which may cause the comparative advantage between regulated and unregulated firms to be reshaped. Second, closely related to the negative economic impact, scholars worry that carbon emissions could leak away through production reallocation or the import of intermediates under the incomplete regulations (Fowlie, 2009). China's Top-1000 program is a textbook example of the "taming the big" strategy. This study aims to shed more light on the impacts of asymmetric regulations.

    Last but not least, this study relates to the literature regarding the economic impact of environmental regulation, which has been and continues to be one of the major areas of interest among environmental economists. Scholars have traditionally argued that stricter regulations harm corporate competitiveness (Gray and Shadbegian, 2003; Greenstone et al., 2012). However, other scholars, mostly from the field of management, hold a more optimistic view. For instance, Porter and van der Linde (1995) argued that an enterprise's cost of complying with environmental regulations could be more than offset by product and process innovations induced by those regulations (for a review, please see Berchicci and King (2007) or Ambec et al. (2013)). As the "paucity of conclusive empirical evidence" has fueled debates in environmental policy-making (Greenstone et al., 2012), this study aims to contribute to this debate.

    The remainder of this paper proceeds as follows. Section 2 provides a background review of China's enterprises' energy-saving programs. Section 3 describes the data and empirical strategies, while Section 4 presents the empirical results. Section 5 provides a battery of robustness check. Finally, Section 6 concludes the study with a discussion of policy implications and future research.


    With decades of rapid economic expansion, China faces growing pressure from energy and environmental constraints. In pursuit of "higher-quality growth," the Chinese government has set ambitious goals to control energy consumption and improve energy efficiency. In terms of the reduction of energy consumption per unit of GDP, the goals were set as high as 20% during the 11th Five-Year Plan (FYP, 2006-2010) and 16% during the 12th Five-Year Plan (2011-2015). One of the key initiatives for realizing the goals was setting a separate energy-saving target for every large manufacturing company. During the 11th FYP, the Top-1000 program was implemented to test the effectiveness of such an initiative. Thereafter, in the 12th FYP period, the scope of the program was expanded to the Top-10,000 Energy-consuming Enterprises Program (hereafter the Top-10k program). Table 1 summarizes the key characteristics of these programs; for a detailed comparison of the two programs, please see Karplus et al. (2016).

    2.1 The Top-1000 Energy-consuming Enterprises Program

    In April 2006, the Top-1000 program was launched by several important central government agencies, including the National Development and Reform Commission (NDRC hereafter) and the National Bureau of Statistics (NBS hereafter). It covers 1008 enterprises in nine energy-intensive industrial sectors: iron and steel, chemicals, power generation, petroleum and petrochemicals, construction materials, non-ferrous metals, coal mining, paper, and textiles. In 2004, these enterprises accounted for 47% of total industrial energy consumption and 33% of total energy consumption in China (NDRC, 2006). All the Top-1000 enterprises are expected to set up a leading group to coordinate energy-saving operations, develop new energy monitoring systems, increase energy efficiency investments, and, most importantly, meet their individual energy-saving targets.

    The energy-saving target for each enterprise is presented as a specific quantity, which is called the amount of energy saved (AES) target. As explained by Zhao et al. (2016), the AES targets are proportionally related to the energy consumption amounts of the enterprises as well as each enterprise's already-achieved energy efficiency level: "for example, if Enterprise A accounts for 0.5% of the total energy consumption for all National Top-1000 Enterprises, then the AES target assigned to Enterprise A will be approximately 0.5% of the AES target for all National Top-1000...

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT