Environmental efficiency evaluation of industrial sector in China by incorporating learning effects

Highlights

• Learning effects are considered to measure industrial environmental efficiency.

• Learning effect is estimated based on cumulative reduction of waste gas emission.

• A slacks-based measure approach incorporating learning effects is proposed.

• The approach is applied to measure industrial environmental efficiency in China.

• Leaning effects significantly affect industrial environmental efficiency of China.

Abstract

Concerns about reducing waste gas emissions and the trade-off with economic development dominate government policy worldwide. However, existing data envelopment analysis does little to permit understanding of how environmental efficiency can be improved by learning solutions. Digesting and following effective strategies from elsewhere is intuitive, but to date no evidence has been able to quantify how exactly the inputs and outputs of production are affected. Developing an improved slacks-based measure model, which incorporates learning effects as one part of undesirable outputs for the first time, we reappraise the performance of Chinese regional industrial production systems. Learning effects have significant impacts for three waste gases, especially for sulfur dioxide, advocating policy which gives managers and operatives access to best practice and empowers them to replicate this within their own firms. As well as reducing output there is strong potential to improve environmental efficiency, particularly in the developed eastern area of China. However, in the central and western regions of China it remains optimal to focus efforts on productivity and there caution on placing too much hope on learning activities is cautioned against. We also find that the proposed approach accompanied with traditional data envelopment analysis model can effectively identify the regional inefficiency whether sourced from learning activities, other production activities, or the both.

Introduction

China's emergence as the “global factory” (Choi and Zhang, 2011) over the past 30 years has brought both prosperity and simultaneously environmental damage. To improve environmental performance the Chinese government has implemented various policies and strategies in recent years, e.g., restricting expansions of energy-intensive companies, increasing the proportion of non-fossil fuels in energy consumption to 11.4% and technology upgrading for desulfurization facilities. The primary cause of waste gas emissions in China has been consumption of energy from fossil fuels. Hence the government must focus concurrently on waste gas emissions, energy consumption and growing productivity from labor and capital. Correctly targeted environmental efficiency improvement may be a cost-effective way to reduce waste gas emissions, increase energy security and maintain economic growth.

Learning can be defined as an organization's capability to improve its performance based on past experience as well as knowledge and understanding (Chang et al., 2013, Kuo and Yang, 2006). As Chang et al. (2013) summarized, learning process involves various knowledge acquisition, knowledge sharing and use; and an organization can create learning opportunities through internal knowledge transfer and new knowledge generation. Learning opportunities can provide effective environments for an organization's members to innovate (Tsai, 2001), and continuous organizational learning may be helpful to change marketing strategies and maintain the sustainable competitive advantage for an organization (Sinkula et al., 1997). Thus, organizational performance can be effectively improved by continuous learning over time (Sorenson, 2003, Chang et al., 2013). According to these studies, learning effect can be defined as accumulative effect of activities associated with learning on organizational performance.

China has placed provision of learning opportunities as a key tenant of policy providing support both domestically and in sending managers, professionals and technicians to learn in other countries. Simultaneously China has invested in waste reductions, reducing waste gas emission abatements from 9.092 billion CNY in the year 2000 to 78.939 billion CNY during 2000–2014 (National Bureau of Statistics of China, 2015). Such investment can encourage innovation (Calantone et al., 2002) and efficiently help organizational members to optimize their behavior to achieve the optimal performance (Garcia-Morales et al., 2007); more learning input delivers better organizational performance (Hsu and Pereira, 2008). Investments in waste gas emission reductions can effectively improve the environmental efficiency of China's industrial system.

Environmental efficiency refers to the ability to create more goods and services while using fewer resources and creating less negative environmental impact (Ramli and Munisamy, 2015). Environmental efficiency is an effective way to measure aggregate economic and environmental performance with respect to energy consumption, offering condensed information for managers to help them cope with environmental issues (Zhou et al., 2008a). In the literature, various approaches have been developed to measure environmental efficiency. A common practice is to first select suitable indicators and then integrate them into a composite index. Extant methods are grouped into parametric and non-parametric methods (Sadjadi and Omrani, 2008). Non parametric techniques, like the data envelopment analysis (DEA) methodology employed here, have the advantage of not requiring specific cost or production functions; misspecification risks are avoided (Wei et al., 2007). Conversely DEA has been widely used in environmental efficiency (Zhou et al., 2008a).

The existing DEA approaches for environmental efficiency evaluation can be generally grouped into five categories. The first is a hyperbolic measure approach proposed by Färe et al. (1989), which is a nonlinear DEA model using a reciprocal measure to evaluate the efficiency with undesirable outputs. The second treats undesirable outputs (pollutants) as inputs to the DEA models (Hailu and Veeman, 2001). The third uses a simple data transformation function, such as the linear monotone decreasing transformation endorsed by Seiford and Zhu (2002) to translate undesirable outputs into normal outputs. The fourth is a directional distance function approach based on the concept of weakly disposable technology, which evaluates and improves decision making unit's (DMU's) efficiency according to the given efficiency improvement direction (Färe and Grosskopf, 2004). Finally a slacks-based measure (SBM) DEA approach is proposed by Tone (2001), and applied by Zhou et al. (2006) to estimate environmental efficiency. This approach evaluates the efficiency in consideration of the values of slacks on all inputs and outputs. Wang et al. (2015) use SBM to estimate the cost of pollution reduction, with Sun et al. (2014) and Wu et al. (2016) considering carbon emission reduction task allocation using SBM.

Though a growing number of studies consider learning effects and organizational performance ours is amongst the first to employ DEA. Badiru and Ijaduola (2009) considers simulation learning whilst Chen and Chang (2010) and Grosse et al. (2015) are amongst the many employing regression techniques. Amongst the few to take advantage of DEA, Chang et al. (2013) considers learning in Taiwanese solid waste recycling, but within their work learning effects remain estimated by regression techniques. Azadeh et al. (2013a) combined use of the DEA and a fuzzy simulation to learning effects for systems operatives, and Azadeh et al. (2013b) use DEA to select strategies for maintenance. All three focus on learning from desirable outputs but here we consider learning from undesirable outputs. Also these extant works however, these studies have incorporated learning effects into DEA models by treating them as an independent indicator or a constraint. Learning effects can also be represented through reduced (or increased) cost, pollutants or energies, which might directly affect existing variables. Lohwasser and Madlener (2012) and Yu et al. (2015) are amongst the few to employ learning effects on pollution reduction, but in these papers environmental efficiency is not an overall performance measure.

Learning effects impact on an industrial system's environment efficiency through three aspects. First, continuous learning for acquiring new knowledge can assist to adjust an industrial system's operation practices and organizational strategy selections. Second, learning can effectively encourage innovations in practical production, cleaner technologies and production management. Third, managers, administrative staff and engineers can acquire experiences through exposure to industrial production and management practices as encouraged by Chinese policy. All can result in more energy saving and less pollutant discharges.

Recent emission reduction policy from the Chinese central government has focused on industrial waste gas, but incorporating risks and supervision has only been actively encouraged since 2011 in 12th five-year plan. This adjusting of policies and regulations could also be regarded as the learning process of national environmental management. Investment in waste gas emission reduction was increased by 371% since the turn of the century, accumulating knowledge which is continually used to improve efficiency (Hsu and Pereira, 2008). According to the scared relative researches as mentioned above, this paper thus employs an improved SBM model which incorporates accumulated learning effects to consider: (1) How best to estimate the impact of learning effects on pollutant reduction (2) How to evaluate environmental efficiency by considering the learning effects and (3) how to quantify potential pollutants reductions? By considering learning effects on undesirable outputs, it is neither an independent variable nor a constraint and can be used to examine impacts of learning effects on environmental efficiency. Thus, we can identify changes in environmental efficiency due to learning effects.

In order to effectively address the above considered issues, we in the current paper proposed an improved approach framework used for learning effect estimation in environmental field. The main innovation of this paper is that, the impact estimation of learning effects on environmental performance under DEA framework is first provided. In the described approach, the pollutant reduction caused by learning effect is first regarded as one part of the undesirable output, but not as an independent variable or constraint. The main methodological contribution of this paper is that, an improved SBM model by incorporating accumulated learning effects estimation is proposed. By estimating learning effects with respect to undesirable outputs, the proposed model can be used to examine the impact of leaning effects on environmental efficiency, and thus it can reasonably evaluate environmental efficiency by eliminating learning effects. The main theoretical contribution is obtained from an empirical study of evaluating environmental efficiency of China's regional industrial systems in 2010 and corresponding environmental policy implications.

The remainder of the paper is organized as follows. Section 2 firstly introduces the basic SBM model for measuring environmental efficiency, and the learning effect estimation approach. Then, the proposed model is provided. The measures of potential industrial waste reductions by considering learning effects are also defined. In Section 3, the proposed approach is applied to examine environmental efficiency of China's regional industrial systems in 2010. Conclusions and some policies suggestions are offered in Section 4.