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Subjects:
Green & Sustainable Science & Technology
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Management
It is crucial to figure out how to achieve sustainable economic growth while fostering the growth of the logistics sector and the economy by expanding low-carbon logistics capability. The term "low-carbon logistics capacity" refers to a logistics operation's capacity to achieve sustainable development in the context of the needs of the low-carbon economy. Research and development capacity, economic development, energy consumption, and other considerations, have an impact on low-carbon logistics capability. Enhancing low-carbon logistics capability does not imply that the logistics sector should cut energy use and pursue a zealous reduction in carbon emissions; rather, it aims to improve the sector's energy use efficiency.
- low-carbon logistics capability
- entropy weight TOPSIS
- dynamic fsQCA
1. Introduction
China’s logistics industry has experienced fundamental changes from its beginning to rapid development after more than 40 years of reform and opening up, and its incredible accomplishments have garnered attention worldwide [1]. Currently, China has the greatest logistics industry in the world, topping all other nations in terms of cargo transport volume and number of shipments, with a total revenue of 12 trillion yuan ($1.74 trillion) in 2021, according to the National Development and Reform Commission. It is apparent that the logistics sector in China has developed into a fundamental and pillar industry, acting as an essential supporter in the growth of the country’s economy.
Despite its rapid development, the logistics sector still faces significant obstacles like high operating costs, inefficient production, high energy consumption, and emissions of carbon and air pollutants. The United Nations Environment Programme reported that the logistics industry accounts for about 10% of total global carbon emissions, with transportation as the largest source of logistics emissions [2]. Furthermore, according to data from the National Bureau of Statistics, China’s logistics industry (transportation, storage, post, and telecommunication) consumed 413.09 million tons of Standard Coal Equivalent (SCE) in 2020, accounting for about 8.29% of the overall energy usage. Obviously, the logistics sector is now a major cause of high energy consumption and serious environmental pollution [3]. Therefore, the intertwined connections between logistics activities and environmental impacts are undeniably significant. As a developing country with the world’s largest carbon emission, China has been committed to carbon reduction. At the 75th session of the United Nations General Assembly in 2020, China formally proposed the goal of achieving carbon peaking by 2030 and carbon neutrality by 2060. Under the background of “dual carbon goals,” promoting the modernization and transformation of the logistics sector, staying away from the current high energy consumption and extensive development, and striving to create eco-friendly, effective low-carbon logistics have all become necessary decisions for the long-term sustainability of the sector.
Building low-carbon logistics capacity, fostering high-quality development of the logistics industry, and protecting the environment have all steadily come under the spotlight in the context of the low-carbon constraint. As one of the important means to mitigate climate change mitigation, low-carbon logistics capacity refers to the integration of environmental sustainability into logistics activities to maintain competitiveness while reducing carbon emissions [4]. By reviewing the literature, several kinds of factors that influence low-carbon logistics capacity can be summarized as economic growth, urbanization, transportation capability, labor force, energy consumption, and governance [5,6,7,8,9]. It is observed that the effects of factors on low-carbon logistics capacity are diverse and may be mutually interactive [4]. A convergence of economic development, energy structure, scientific and technical innovation, and other influencing elements has led to the creation of green logistics [10]. However, there is currently little research that has looked into the interactions and combined effects of multiple factors (especially three or more) [11,12]. Additionally, previous research typically applied techniques like structural equation modeling, econometric models, data envelop analysis, and other quantitative or qualitative methods to examine the key factors affecting low carbon logistics capacity. Traditional quantitative approaches that aim to investigate marginal effects, such as multiple regression analysis, interpret the multiple causations between various variables. On the other hand, obtaining interaction effects for more than three variables is arduous [13]. Likewise, these methods are limited in their ability to deal with causal complexity on a holistic level as well as uncover individual variation observed in reality [14]. Therefore, for these reasons, this paper attempts to overcome these restrictions by using the dynamic fuzzy-set qualitative comparative analysis (fsQCA) approach, which emphasizes correlations between sets of antecedents and the outcome while also making explicit the configuration of factors [14].
In his book, Ragin [15] provided a thorough explanation of fuzzy-set qualitative comparative analysis (fsQCA), including a discussion of the problems that come with using a set-theoretic approach, which was considered a new path for management research [16]. Recently, fsQCA has been used in logistics research to explore the configurational paths that influence the growth of the logistics industry [17,18]. The main data used in mentioned studies was cross-sectional data, which excluded temporal influences. Some fsQCA analyses have been performed to take into account the time dimension by utilizing a method known as panel data fsQCA or dynamic fsQCA, which was created by Garcia-Castro and Ariño [19]. Consequently, dynamic fsQCA is used to analyze the joint effect of determinants on low-carbon logistics capacity, not only in the assessment of the stability of configurational paths for an outcome but also in the degree of fluctuations in the path associated with different provinces over time and the antecedents that make up these paths [20].
2. Factors Affecting Carbon Emission in Logistics Industry
Four basic categories can be drawn from previous research on the factors affecting carbon emissions in the logistics sector. The first is in terms of energy inputs directly consumed by logistics, including energy structure, energy intensity, energy efficiency, and energy prices [21,22,23,24]. Secondly, it is from macroeconomic factors, including industrial structure, economic scale, urbanization rate, cargo turnover, environmental regulation, and financial support [22,24,25,26]. Thirdly, it is considered from the logistics industry content, including logistics transportation structure, logistics transportation intensity, logistics industry labor productivity, logistics industry science and technology innovation capacity, logistics industry output scale, employee scale, and logistics enterprise average scale [22,24]. Finally, in terms of industry management degree, including the economic development of the service industry, the use of low-carbon technology in various sectors of the economy, and industry efficiency [27].
3. Factors of Low-Carbon Logistics Capacity
Given that the expansion of the logistics sector unavoidably worsens greenhouse gas emissions and endangers ecological sustainability while fostering economic growth. Therefore, as a potential remedy for the realistic dilemma facing the logistics industry, low-carbon logistics capacity enhancement and assessment index development have received a lot of attention in recent years. To examine the role of low-carbon logistics development in promoting low-carbon economic growth, Wang [28] constructed a regional low-carbon logistics capacity index system with a low-carbon logistics environment, low-carbon logistics strength, and low-carbon logistics potential as elements and empirically analyzed a regional sample using the fuzzy matter-element method. Li et al. [29] constructed an evaluation system of regional logistic low-carbon competitiveness from three aspects, including low-carbon logistics competitive environment, service capability and development level and measured the degree of influence of each index on regional logistics low-carbon competitiveness by projection pursuit method. Similarly, Wang et al. [4] used the entropy technique to assess regional low-carbon logistical development capacity in terms of infrastructure capacity, environmental protection capacity, business expansion capacity, and low-carbon ecological level. Zhou et al. [7] created a sequential parametric index system with infrastructure support capacity, information system guarantee capacity, operation management and operation capacity, and low-carbon ecological development capacity as elements with the intention of analyzing the interaction status between regional low-carbon logistics capacity subsystems and the degree of coordination between them, who came to the conclusion that the coordination level of each subsystem index fluctuates significantly over time. Correspondingly, from previous studies, each factor not only affects low-carbon logistics capacity but also interacts with other factors to have an impact as a whole. Additionally, due to the time shift, their impact on low-carbon logistics capacity will fluctuate.
4. fsQCA in Logistics Issues
Fuzzy-set qualitative comparative analysis (fsQCA) is one the widest and most emerging research technique to analyze the combined impact along with correlation to identify the configurations [11,30]. Asymmetry equations and causal complexity in the context of fsQCA lead to multiple paths leading to the same outcome with various combinations [31]. Therefore, fsQCA is widely applied in the analysis of logistics issues. Hartmann et al. [32] used fsQCA to explore how drivers at multiple levels interact to shape the fleet decisions in one of Europe’s leading third-party logistics providers operating a large, multi-country road transportation network. The study of Vlachos [33] was to empirically study the necessary and sufficient antecedents of customer loyalty to logistics service providers (LSPs) by using fsQCA. Moreover, the fsQCA was applied to identify the causal configuration relations for higher values of economic development by considering the influence of logistics competitiveness and logistics cost [34]. Despite the fact that fsQCA has been utilized extensively in previous research, the data it uses do not take into account temporal impacts [35]. As a result, some academics have lately begun to take these impacts into account by inventing dynamic fsQCA and using it to conduct research in the field of environmental pollution [14], entrepreneurial attitudes [35], and R&D intensity [36].
5. Entropy Weight TOPSIS
Entropy weight TOPSIS, which combines entropy and TOPSIS, is a thorough evaluation technique. An objective weighting approach to give weights to each index is the entropy weight method, which introduces the idea of information entropy [37]. The term “entropy” comes from physics and describes the level of intrinsic chaos in a system. Using the concept of information entropy, the more chaotic a system is, the greater the degree of uncertainty, the less information it can carry, and the lower its weight [38]. Compared to the weighting methods such as AHP, ANP, and DEMATEL, which require expert scoring to determine the weights, the entropy weighting method is more objective. Furthermore, some novel methods were proposed in the last decade to assign a weight, such as BWM, CILOS, and IOCRIM [39]. Entropy uses the information carried by the entropy value of the data itself to calculate the weights, according to the level of numerical dispersion across indicators, to provide a basis for the comprehensive evaluation of multiple indicators and make the research findings more unbiased and fair.
The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is a comprehensive distance-based evaluation method, first proposed by Hwang and Yoon in 1981, which is known as the “superior-inferior solution distance method” [37]. The method uses the proximity of the evaluation alternative to the idealized target to rank the merits of each evaluation alternative [40]. Among the evaluation methods of PROMETHEE, VIKOR, and Fuzzy AHP, TOPSIS has a relatively simple calculation algorithm, that can analyze quantitative data and fully use data information [41]. Entropy weighting, combined with the TOPSIS method, has been widely used in various evaluation-based studies [42,43]. In addition, with reference to the logistics context, entropy weight TOPSIS was applied to the issue related to the regional logistics industry’s high-quality development level measurement and green logistics partner selection [44,45].
To sum up, according to the analysis of the above three parts, there are numerous pieces of literature on the low-carbon logistics capacity with different influencing factors and methods. Nonetheless, there are relatively few studies on the multi-factor interaction and low-carbon logistics capacity. Additionally, the temporal effects are absent from the causal combination of the variables affecting low-carbon logistics capacity using fsQCA. In light of this, an entropy weight TOPSIS is used to assess the provinces’ capacity for low-carbon logistics.
This entry is adapted from the peer-reviewed paper 10.3390/su151411372
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