The effects of a Smart Logistics policy on carbon emissions in China: A difference-in-differences analysis

Highlights

• A difference-in-differences (DID) model was constructed in this study.

• The effect of Smart Logistics policy on China’s carbon emissions was investigated.

• Freight volume, employment, and total social retail are key influencing factors.

• The Smart Logistics policy can restrain carbon emissions significantly.

• The policy has a continuous impact on carbon emissions in the second year.

Abstract

The rapid development of logistics has increased carbon emissions. Smart Logistics distribution system is a comprehensive logistics system supported by advanced information technology, which aims to improve the operation efficiency of the logistics industry and reduce carbon emissions by optimizing resource allocation. Therefore, it is of great significance to explore the effects of Smart Logistics policy (SLP) on China's carbon emissions. This paper used the binary choice model to investigate the main factors influencing the establishment of Smart Logistics in Chinese cities, and whether carbon emissions are the cause of the establishment of Smart Logistics. Secondly, it analyzed the effect of SLP on carbon emissions using a difference-in-differences (DID) model. The results reveal that freight volume, logistics employment, and total social retail are important factors determining whether a city should establish Smart Logistics or not. Additionally, the decision whether to establish Smart Logistics is an exogenous policy variable to carbon emissions. The implementation of SLP can restrain carbon emissions significantly, with a continuous impact in the second year. Based on the findings of this paper, a series of policy implications with respect to promote the development of Smart Logistics were proposed.

Introduction

In recent years, driven by a series of national policies and market demands, China's Logistics sector has developed rapidly. As an emerging complex industry, the modern Logistics sector can deepen the links between various regions, and play an important role in promoting industrial adjustment, transforming economic development modes, and enhancing national economic competitiveness (Malhotra and Mishra, 2019). More specifically, China's total social logistics in 2018 was RMB 283,100 billion, a year-on-year increase of 6.4% at comparable prices. In the past three years, the average annual growth rate of total social logistics has reached 11.01%. The ratio of total social logistics costs to GDP in 2018 was14.8%, an increase of 0.2 percentage points over the same period last year. The logistics industry overall has been operating well (Lyu et al., 2019). Logistics is an important link between production and consumption, the logistics sector consumes a lot of primary and secondary energy sources, such as coal, gasoline, diesel, fuel oil, natural gas, heat, and electricity. The logistics industry in China has maintained an extensive development mode, which has led to huge energy consumption by the industry (Dai and Gao, 2016). According to the IPCC Fifth Assessment Report, if the logistics industry does not change its existing high energy consumption development mode, its energy consumption will be 80% higher by 2030 than it is now (IPCC, 2014), and high energy consumption in the logistics industry means high emissions (McKinnon, 2012). Although the role of the logistics industry in driving the national economy has strengthen gradually, it has caused a series of environmental problems such as energy consumption, carbon emissions, and air pollution (Chen and Bidanda, 2019, Van Woensel et al., 2001, Konur and Schaefer, 2014). In face of the worldwide demand for environmental protection, it is a top priority for the Chinese government to formulate effective climate policies to curb domestic carbon dioxide emissions (Song et al., 2019). Developing low-carbon logistics and transforming the development model of the logistics industry is the premise and foundation for realizing a low-carbon economy, which is related to whether the entire economic environment can achieve low carbon emissions (Yang et al., 2019).

The logistics industry is the carrier of the industrial system and the basic industry of the national economy. The transformation of traditional logistics to Smart Logistics directly affects the quality and speed of China's economic development and the improvement of China's competitive advantage (Shu et al., 2016). The development of the logistics industry can enhance the integration and penetration between industries, promote the transformation of industrial production processes, accelerate the adjustment of industrial structure and technological upgrading and innovation, so as to improve energy efficiency and save resources. The concept of “Smart Logistics” can be traced back to the “smart supply chain”, proposed by IBM for the first time in 2009. It refers mainly to improving the intelligence and automation level of the entire logistics system through intelligent technologies and means (Kirch et al., 2017). The development of Smart Logistics distribution system is an important support for adapting to flexible manufacturing, promoting consumption upgrading, achieving precise marketing, and promoting the development of e-commerce (Barenji et al., 2019), it is also the development trend of the logistics industry and the commanding heights of competition in the future (Karakikes et al., 2019). In 2015, the Ministry of Commerce researched and formulated the “Notice of the Ministry of Commerce on the Implementation Plan of the Smart Logistics Distribution System”, and announced the first batch of Smart Logistics distribution demonstration cities. The aim of establishing a Smart Logistics demonstration city is to accelerate the construction of a Smart Logistics distribution system, reduce logistics costs, and improve energy efficiency, thereby reducing carbon emissions. Smart Logistics provides an innovative and more efficient mode of Logistics, but whether the implementation of SLP will effectively curb the growth of energy consumption and restrain the carbon emission remains to be known. It is an important issue that needs to be clarified in the context of the rapid development of Smart Logistics in order to provide evidence to support the development of Smart Logistics in the future. Based on the considerations, this paper aims at empirically evaluating the inhibitory effect of SLP on carbon emissions.

With the beginning of strategic research on Industry 4.0, scholars have developed various definitions of Smart Logistics. In short, Smart Logistics is the supervision of materials, information and finances in a process from supplier to manufacturer (Tiejun, 2012). In addition, spatial references need to be included in the definition. Smart Logistics aims to effectively adjust planning and schedule. Information and communication technology, infrastructure, people, and governmental decisions are the four main pillars of Smart Logistics (Jabeur et al., 2017). The application of intelligent logistics in economic practice includes: intelligent services and products, a technology-based and easy-to-change method, “decentralizing” labor to smart devices, communication and cooperation with the entire environment, data processing and sharing, settlement and payment (Uckelmann, 2008). From the perspective of the end user, the data and new services generated on the basis of the terminal equipment, access networks and the back-end infrastructure constitute the greatest value of Smart Logistics (Iwan and Malecki, 2015). Korczak and Kijewska (2019) conducted a systematic review of the ideas of Smart Logistics in the field of research literature, identified the goals and tasks of Smart Logistics in Industries 4.0, and pointed out the potential of using Smart Logistics in the development of smart cities. Despite all the reviews, it is difficult to formalize a definition for Smart Logistics.

Many studies investigated the impact of Smart Logistics construction on the transportation efficiency and supply chain performance. The construction of smart logistic is considered to be an effective measure to solve the current transport efficiency problem (Banister, 2004). Harris et al. (2015) analyzed the positive impact of new generation information and communication technologies such as cloud computing and the Internet of Things on multimodal transportation. They showed that recent technological developments might lower the barriers to multimodal ICT adoption and lead to a more integrated freight network. Oonk (2016) believed that the logistics sector is most likely to benefit from the transportation-centric development of intelligent transportation systems (ITS), such as increased vehicle automation, platooning, and energy-efficient intersection control. The advantages of ITS in the field of pre-travel optimization of network transmission could be even greater. Costa et al. (2016) introduced the concept of Smart Cargo, which can automatically respond to its context, find and understand alternatives, calculate adaptive behavior, and optimize its own decisions. Smart Cargo also allows intermodal transport for real-time situational awareness and response. From the perspective of supply chain performance, some scholars dealt with how to adopt the Smart Logistics to support the promotion of Supply chain performance. Zelbst et al. (2010) used the sample data from 155 manufacturing and service organizations to assess the model, the research results showed that the use of RFID technology could improve the sharing of information among members of the supply chain, which led to the improvement of supply chain performance. In addition, advanced supply chains transform traditional systems into intelligent systems and use international standard modeling methods to develop business processes and information models, which enabled all parties in the supply chain to improve their work efficiency by referencing these models (Ahn et al., 2016). Isiklar et al. (2007) proposed an effective third-party logistics (3PL) evaluation and selection intelligent decision support framework, which can deal with uncertain and inaccurate decision situations. The framework can more accurately, flexibly and efficiently retrieve the 3PL service providers that are most similar to the current decision-making situation.

The current research on Smart Logistics is focused mainly on the concepts of Smart Logistics, as well as the impact on the transportation efficiency and supply chain performance. There are also studies analyzing the influencing factors of carbon emissions in the logistics industry (Papagiannaki and Diakoulaki, 2009), studies have proven that emission reduction policies (e.g. Daryanto et al., 2019), social preference (e.g. Xia et al., 2018), procedure and logistics decision (e.g. Kaur and Singh, 2017), vehicle routing (e.g. Behnke and Kirschstein, 2018), carbon emission tax (e.g. Choi, 2013) and other factors are all important factors affecting the carbon emissions of logistics industry. Although there are a growing number of literatures about Smart Logistics, few investigations into the effects of SLP on China’s carbon emissions has been analyzed, and it is unclear whether the construction of a Smart Logistics city can play a role in curbing the growth of carbon emissions. To fill this gap. This paper aims to examine the effect of SLP on China's carbon emissions. Firstly, this paper analyzes the main factors influencing the construction of Smart Logistics for a city using the binary choice model and whether the carbon emission is the reason for the establishment of urban Smart Logistics. Secondly, using DID analysis, this paper identifies the impacts of SLP on carbon emissions. Recently, Chinese researchers and government agencies have increasingly adopted policy assessments, especially third-party policy assessments (Adelle and Weiland, 2012). The DID method is widely used in third-party assessments (Wang et al., 2018). DID method and natural experiment were first introduced by western economists in the late 1970s (Park et al., 2013). With the accumulation of previous research, the interpretation of economic issues by the DID method has become increasingly mature. Scholars have applied DID methods to study problems in the fields of health, education, environment (Younis et al., 2019, Baier and Helbig, 2014, Cisilino et al., 2019). Zhou and Chen (2005) used the DID method to analyze China's economic problems for the first time. They regarded rural tax and fee reform as a “quasi-experiment” and studied the policy effect of tax and fee reform through the DID model. Afterward, there has been a large number of literatures on policy evaluation using DID models. However, few studies have employed this approach for assessing how SLP influences the carbon emissions for the case of China.

The rest of this study is organized as follows. Section 2 outlines the policy context. Section 3 introduces the methodology and data used in the empirical analysis. Section 4 shows the empirical results. The last section ends with the conclusions and policy implications.