**1. Introduction**

China has entered a new stage of high-quality development; the people's demand for ecological environment is getting higher and higher, and the importance and urgency of promoting green development has become more and more prominent. In 2020, General Secretary Xi Jinping solemnly declared to the world at the United Nations General Assembly that China' s carbon dioxide emissions will peak by 2030 and strive to achieve carbon neutrality by 2060. As a high-end service industry, logistics has the characteristics of high energy consumption and high emission. The development path of logistics must follow low-carbon development, focusing on green logistics, low-carbon logistics and intelligent informatization. With the rise of the low-carbon revolution and the official advocation of green environment at the Copenhagen environment conference, low-carbon logistics has become the focus of academic research at home and abroad. The research of low-carbon logistics focuses on four aspects: carbon emission accounting, carbon emission driver identification, low-carbon logistics capability measurement and low-carbon logistics development strategy. In carbon emission accounting of logistics process, Butner K, Dada A, Piecyk M I adopt the method of carbon emission measurement based on whole life cycle and design the analytical of carbon emission measurement including structural factors and

**Citation:** Guo, Z.; Tian, Y.; Guo, X.; He, Z. Research on Measurement and Application of China's Regional Logistics Development Level under Low Carbon Environment. *Processes* **2021**, *9*, 2273. https://doi.org/ 10.3390/pr9122273

Academic Editors: Luis Puigjaner, Antonio Espuña Camarasa, Edrisi Muñoz Mata and Elisabet Capón García

Received: 17 November 2021 Accepted: 10 December 2021 Published: 17 December 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

commercial factors [1–3]. Wang LP and Liu Y calculated the carbon emission from energy data of Chinese provinces from 1997–2004 and 2007–2013 [4,5]. Concerning identifying drivers of carbon emissions in the logistics industry, Timilsina and others studied on the growth of carbon emissions in the transport in selected Asian countries from 1980 to 2005 [6]. Lei Yang takes Shenzhen port as an example and measures the carbon emission in the port comprehensive logistics system [7]. Yang YW, Li FG, Men D et.al explore the driving causes of carbon emission growth by using LMDI model decomposition analysis [8–10]. In the low carbon logistics capability, Jessica Wehner takes an interactive approach to capacity utilization to contribute to sustainable freight transport and logistics [11]. The Chinese scholars mainly focus on the fuzzy comprehensive evaluation method, entropy weight TOPSIS model, DEA evaluation model, and Malmquist model static measurement methods to evaluate [12–15]. In the development strategy of low-carbon logistics, relevant scholars analyze the current situation and problems of low-carbon logistics development from different perspectives and put forward suggestions to promote the development of low-carbon logistics [16–20]. At present, scholars have conducted fewer studies related to the lowcarbon development of regional logistics. Ma YY used data envelopment analysis to study the total factor productivity of China's logistics industry under low-carbon constraints [21]. Xie F and Gao FF analyzed the low carbonization of China's logistics industry and related industries by constructing an index system for the coordinated development of logistics industry and low carbon economy and using a coordinated development model [22,23]. Yu Q analyzed the logistics efficiency and its influencing factors in 30 provinces and cities, as well as the eastern, central and western regions of China based on the DEA-Tobit two-stage method [24]. Song Lina used a combined model of principal component analysis and data envelopment analysis to evaluate the regional low-carbon logistics performance of provinces along the Silk Road Economic Belt in China [25]. Wang X, taking Anhui Province as an example, explored the mechanism of the low-carbon development of regional logistics using the theoretical analysis framework of "development dynamics-measurement criteria-acting subject" [26].

To solve the fuzzy and stochastic problems in the process of low carbonization evaluation of regional logistics, the fuzzy and stochastic properties were converted into a definite value by the cloud generator, which broke the limitation of qualitative and quantitative research and made the evaluation more hierarchical [27].

#### **2. Theoretical Basis**

#### *2.1. Entropy Weighting Method*

Entropy is a measure of the disorder degree of a system. According to defined entropy, we can use the size of entropy to judge the discreteness degree of an index. The smaller the entropy value is, the greater the influence of the index on the comprehensive evaluation (i.e., the weight). Therefore, information entropy is a tool that can be used to objectively empower multiple signs to provide the basis for a comprehensive evaluation:

1. Standardized processing of data: assume that m evaluation objects, *n* evaluation signals, ge<sup>t</sup> the original evaluation, *X* = *Xij m*×*n*, make

$$
\Omega I\_{ij} = \begin{cases}
\begin{array}{c}
\frac{X\_{ij} - \min\_{\bar{i}} X\_{ij}}{i} . \text{Negative indicator} \\
\frac{\max X\_{ij} - \min\_{\bar{i}} X\_{ij}}{i} . \text{Y}\_{ij}
\end{array}
\end{cases}
\tag{1}
$$

where *Xij* denotes the *j* indicator of the *i* evaluator in a given locality, *Uij* it is the standardized data.

2. Calculation of weights for each indicator:

$$P\_{ij} = \frac{\mathcal{U}\_{ij}}{\sum\_{i=1}^{m} \mathcal{U}\_{ij}} \tag{2}$$

3. Calculation of entropy for each indicator:

$$\mathcal{e}\_{\dot{\jmath}} = -\frac{1}{\ln m} \sum\_{i=1}^{m} P\_{\dot{\imath}\dot{\jmath}} \ln P\_{\dot{\jmath}\dot{}} \tag{3}$$

4. Determination of weights for each indicator:

$$w\_j = \frac{1 - \mathfrak{e}\_j}{n - \sum\_{j=1}^{n} \mathfrak{e}\_j} \tag{4}$$

#### *2.2. Cloud Models*

#### 2.2.1. The Cloud Models

Li DY and others are the basis of cloud computing, reasoning, and control, and it is a model for the transformation of uncertainty between qualitative concepts and quantitative descriptions [27–29]. It is widely used in risk assessment, data mining, and performance evaluation and so on [30–33]. Let *O* be a quantitative set represented by a numerical value. *I* is a qualitative concept in *O* space. If the quantitative value *x* ∈ *O* and *x* is a stochastic implementation in the qualitative concept *I*, the determinacy of *x* to *I*: *μ*(*x*) ∈ [0, 1], It is a stochastic number with a tendency to stability:

$$
\mu: O \to [0,1], \forall \mathfrak{x} \in O, \mathfrak{x} \to \mu(\mathfrak{x}).
$$

Then the distribution of *x* in the set is called the cloud model, with each *x* being a cloud drop.

#### 2.2.2. Numerical Characteristics of Clouds

Cloud models represent the primitive-language values in natural language, and the three numeric features of cloud models—*Ex* (expectation), *En* (entropy), and *He* (supers entropy)—represent the numerical characteristics of language values, thus achieving the goal of integrating the fuzziness and randomness of objects studied. Among them, *Ex* is the expectation of cloud droplet distribution in the domain. It is the central value of cloud droplet in a given set space distribution. *En* indicates the uncertainty measure of qualitative concept, which reflects the dispersion degree of cloud droplet, which is determined by the ambiguity and randomness of qualitative concept. *He* is a measure of the fuzziness of entropy, the size of which indirectly reflects the thickness of cloud droplets and the fuzziness and randomness of entropy [34–36].

#### 2.2.3. Cloud Generator

The mutual transformation between qualitative concept and quantitative data in cloud model needs to be realized by cloud generator. Typically, a cloud generator includes a forward cloud generator, a reverse cloud generator and a conditional cloud generator.

Forward Cloud Generator: A mapping from a qualitative concept to a quantitative value, a process by which cloud droplets are generated from the numerical eigenvalues of a cloud model, as shown in Figure 1.

**Figure 1.** Positive cloud generator.

In Figure 1, *CG* means the forward cloud generator, *xi* is the cloud droplet, and *μi* is its affiliation degree.

Reverse Cloud Generator: Mapping from quantitative values to directed ideas, that is, converting exact data into the suitable qualitative language (*Ex*, *En*, *He*), as shown in Figure 2.

**Figure 2.** Inverse cloud generator.

In Figure 2, *CG*−<sup>1</sup> notes a reverse cloud generator, *xi* is the cloud droplet, and *μi* is its affiliation degree.

*X* Conditional Cloud Generator: In the numerical domain space of a given set, the three digital eigenvalues of the known cloud, *Ex*,*En*,*He*, and contain a specified condition *x* = *x*0, this is called Conditional Cloud Generator. As shown in Figure 3.

**Figure 3.** Conditional cloud generator.

#### *2.3. Carbon Emission Measurement*

At present, there is no uniform standard for carbon emission measurement in the world. This paper adopts the more extensive estimation method of IPCC, also known as the IPCC inventory coefficient method. This method is based on the final energy consumption, and considering the waste gas emitted during the logistics process includes not only carbon dioxide, but also carbon monoxide, hydrocarbons, etc. In this paper, the carbon emission of the logistics industry is estimated by energy consumption. This is done by multiplying the various energy consumption of the logistics industry by their respective standard coal coefficient and then by their respective carbon emission factors to arrive at the total carbon emissions for a given year in the region:

$$\mathbf{C} = \sum\_{i} \mathbf{C}\_{i} = \sum\_{i} \delta\_{i} \theta\_{i} E\_{i} \tag{5}$$

Of which: *Ci* means carbon emissions from type *i* energy sources, *Ei* denotes consumption of type *i* energy sources, *θi* marks coefficient of fractional standard coal for type *i* energy sources, *δi* stands for carbon emission factors for type *i* energy sources, and *θiEi* denotes amount of fractional standard coal for type *i* energy sources.

#### **3. Regional Logistics Decarbonization Development Evaluation Model Construction**

#### *3.1. Evaluation Index System for Low-Carbon Development of Regional Logistics*

Low-carbonization of regional logistics means building a regional logistics system which is based on low-carbon economy and green logistics and supports the concept of "sustainable development" and"carbon emissions". It meets the regional economic and political development and has a supporting system of logistics information and organization and operation, while possessing the characteristics of green, balanced and efficient. Related scholars have different focuses and starting points for the research on the level of regional logistics decarbonization, such as Lai, Ma Shihua et al. from the logistics system [37,38], and Daugherty and Wang Ming from the level of enterprises [39,40] to define the low carbon logistics capacity. This paper argues that the level of logistics at the regional level is essentially a kind of competitiveness, which should not only focus on the current existing strengths, but also on the potential for future development, and should pay attention to

both its own capacity building and the influence of the growth environment. According to the *China Logistics Development Report 2019–2020* and the Low Carbon Logistics Development Guidelines, the low-carbon logistics development focuses on the following subjects: railway freight transport, low-carbon automobile transport, logistics rationalization, common distribution, recycling of waste facilities, green packaging, industrial waste disposal and information e-commerce. According to the quantitative nature of the action guide and the availability of data, following the principles of systematism, scientificity and application of the selection of indicators, this paper summarizes three first-level indicators to evaluate the level of regional logistics decarbonization. Low-carbon logistics environmental support is an external factor that affects the level of regional low-carbon logistics capacity, which is influenced by the economic and policy environment. Low-carbon logistics environmental support is to evaluate the existing competitiveness of regional logistics low-carbon development, mainly in terms of infrastructure construction, logistics industry scale and logistics industry efficiency. The potential of low-carbon logistics is the sustainable driving force for the decarbonization of regional logistics, which includes the potential of regional logistics in terms of input, output and demand, and is mainly measured by the growth rate indicator. The specific indicators are shown in Table 1.


**Table 1.** Regional Logistics Decarbonization Evaluation Index System.

#### *3.2. Construction of Evaluation Model*

#### 3.2.1. Defining the Object and Domain of Cloud Model Evaluation

The evaluation object is established as the regional logistics decarbonization evaluation, showed by *X*. According to the regional logistics index evaluation index system constructed in Table 1, the factor domain of the criterion layer is determined as *X* = {*<sup>X</sup>*1, *X*2, *<sup>X</sup>*3}, and the index layer domains are *X*1 = {*<sup>X</sup>*1,1, *<sup>X</sup>*1,2, ··· , *<sup>X</sup>*1,5}, *X*2 = {*<sup>X</sup>*2,1, *X*2,2, ··· , *<sup>X</sup>*2,10} and *X*3 = {*<sup>X</sup>*3,1, *X*3,2, ··· , *<sup>X</sup>*3,7}.

#### 3.2.2. Settle the Evaluation Level of Each Indicator

For each index evaluation level domain A, to more clearly represent the average level of the research object and the degree of distinction, the general number of levels *p* is an odd number not greater than 7. Therefore, this paper divides each evaluation index into 5 levels according to the relevant literature and index characteristics: *T* = {*low*, *lower*, *general*, *higher*, *high*}.

#### 3.2.3. Decide the Cloud Numerical Eigenvalues of Each Evaluation Index and Cloud Model Map

Factor evaluation is carried out between the various hierarchical domains corresponding to each evaluation indicator, and the fuzzy relationship matrix is obtained by generating cloud numerical eigenvalues through a forward cloud generator. Let the upper and lower critical values of the rank *Tk*(*k* = 1, 2, ··· , *p*) corresponding to the evaluation indicator *j*(*j* = 1, 2, ··· , 21) be [*<sup>G</sup>*min, *<sup>G</sup>*max]. The normal cloud model for the rank *k* corresponding to the evaluation indicator *j* is

$$E\mathbf{x} = (\mathbf{G}\_{\rm min} + \mathbf{G}\_{\rm max})/2\tag{6}$$

The critical value is the transition value of two adjacent levels, which belong to the two corresponding levels at the same time, so the affiliation of the two levels is equal:

$$\exp\left[-\frac{\left(G\_{\text{max}} - G\_{\text{min}}\right)^2}{8\left(E\_{\text{fl}}\right)^2}\right] = 0.5\tag{7}$$

$$En = \frac{G\_{\text{max}} - G\_{\text{min}}}{2.355} \tag{8}$$

The super entropy *He* reflects the thickness of the cloud layer, which is a measure of the uncertainty of entropy, and the final value is determined by repeated trials according to the magnitude of entropy. According to the obtained fuzzy relationship matrix, MATLAB programming is applied to obtain the cloud model map corresponding to each metric.

#### 3.2.4. Determine the Affiliation of Each Evaluation Index

Using *X* conditional cloud generator, we calculate the affiliation degree of each index corresponding to different levels, form the corresponding cloud model affiliation matrix. Select the largest affiliation degree as the evaluation level of the index. The corresponding cloud affiliation degree is

$$w\_{jk} = \exp\left\{-\frac{\left(x\_0 - Ex\right)^2}{2\left(En'\right)^2}\right\} \tag{9}$$

where *En* is a normal random number with *En* as the expected value and *He*<sup>2</sup> as the variance, i.e., *En* ∼ *<sup>N</sup>*(*En*, *He*<sup>2</sup>). The affiliation matrix is denoted as *V* = ( *Vjk*) *n*×*p* and *Vjk* denotes the affiliation of the *k* th rank of the *j* th evaluation index, and in order to optimize the evaluation accuracy, the average of different affiliations under the repeated *N* times conditional cloud generator is used, i.e.,

$$V\_{jk} = \frac{1}{N} \sum\_{q=1}^{N} v\_{jk}^{q} \tag{10}$$

#### 3.2.5. Entropy Weighting Method to Assign the Index Weights

According to the calculation steps of the entropy weighting method mentioned in 2.1 above, the weighting values of each indicator are determined in conjunction with the regional logistics low carbon development evaluation index system.

#### 3.2.6. Determine the Comprehensive Evaluation Level of Regional Logistics Decarbonization Development

In this paper, the comprehensive determination degree of regional logistics decarbonization development level is obtained according to the following formula.

$$\mathbb{C}\_{ik} = w\_j V\_{jk}^i (i = 1, 2, \cdots, m) \tag{11}$$

where *Vijk* is the affiliation degree of an region in year *i* and *wj* is the weight of the index. According to the principle of maximum degree of certainty, the level where the maximum degree of certainty is selected is the final comprehensive evaluation level of regional logistics decarbonization development.

#### **4. Empirical Analysis and Pathway Study**

As a pioneer area of green and low-carbon development in China, the development of low-carbon logistics in Beijing, Tianjin and Hebei can play a typical demonstration and promotion role in the country. Beijing, Tianjin and Hebei have significantly different logistics capabilities due to their regional characteristics and differences in economic and political levels, and it is urgen<sup>t</sup> to establish a mechanism for the collaborative development of low-carbon logistics in the region. Therefore, this paper takes Beijing, Tianjin and Hebei as an example to evaluate the development of low-carbon logistics in each region, find out the differences between them, and then discover the main factors affecting the development of low-carbon logistics in each city, so as to provide theoretical support for the development of low-carbon logistics.

#### *4.1. Data Sources and Carbon Emission Measurement in Beijing, Tianjin and Hebei*

#### 4.1.1. Data Sources

This paper analyzes the data of Beijing-Tianjin-Hebei region from 2013 to 2019 as samples, and the relevant raw data are obtained from the annual data of National Bureau of Statistics by province, *China Economic Statistical Yearbook* and *China Energy Statistical Yearbook*.

#### 4.1.2. Carbon Emission Measurement in Beijing, Tianjin and Hebei

According to the relevant data of China Energy Statistical Yearbook, the logistics industry in Beijing, Tianjin and Hebei mainly consumes 11 types of energy, including raw coal, gasoline, kerosene, diesel, fuel oil, liquefied petroleum gas, natural gas, liquefied natural gas, heat, electricity and other energy sources; among them, the carbon emission coefficients of liquefied natural gas, heat and other energy sources have not been found for the time being, and the consumption of these three types of energy sources accounts for a small part. The carbon emission coefficients of LNG, heat and other energy sources are not available, and these three types of energy sources account for little consumption, so their carbon emissions are not counted. Due to limited space, the raw data are shown in Table 2, taking the Beijing area as an example.


**Table 2.** Energy consumption by region in Beijing.

According to the *2006 IPCC Guidelines for National Greenhouse Gas Inventories*, the reference coefficients for the conversion of standard coal and carbon emission coefficients for various energy sources are shown in Table 3.

**Table 3.** Reference factors for the conversion of standard coal and carbon emission factors for various energy sources.


Substitute the data into Equation (5) for calculation to ge<sup>t</sup> the carbon emissions of each region from 2013–2019, which are divided by the unit value added of logistics industry as the raw data of carbon emissions per unit value added of indicator logistics industry and show the calculation results in Table 4.

**Table 4.** Carbon emissions from Beijing, Tianjin and Hebei regions.


*4.2. Evaluation of the Low Carbon Development of Logistics in Beijing, Tianjin and Hebei*

4.2.1. Selection of Indicator Samples

According to the index system constructed in Table 1, select the relevant statistics of Beijing, Tianjin and Hebei to analysis, and the following table takes the raw data of Beijing as an example, as shown in Table 5.


**Table 5.** Raw data of each indicator in Beijing.

Note: To ease the subsequent ranking, the data related to the negative item is added with a negative sign to make it a positive indicator.

#### 4.2.2. Determine the Level of Each Evaluation Index

In this study, the domain was divided into five evaluation levels, and the maximum and minimum values of the indicator data of 31 provinces were taken as the range of evaluation factors, and then the range was reasonably divided into five levels to determine the upper and lower critical values [*<sup>G</sup>*min, *<sup>G</sup>*max] of each level, and the results are shown in Table 6.


4.2.3. Determine the Cloud Digital Characteristic Value of Each Evaluation Index and Cloud Model Map

According to the ranking of each indicator in Table 6, the upper and lower critical values [*<sup>G</sup>*min, *<sup>G</sup>*max] were substituted into Equations (6)–(8) to obtain the numerical characteristic values of the cloud model for each indicator, as shown in Table 7. The number of cloud drops per cloud was set to 3000, and the cloud model plots for each evaluation metric were derived by plotting the normal cloud model with MATLAB software. The cloud model diagrams for each of the five evaluation indicators included under the level of low carbon logistics environmental support are shown in Figures 4–8, for example. The horizontal coordinates represent the range of values of the evaluation factors, the vertical coordinates represent the corresponding affiliation degrees, and the curves from left to right represent the clouds represented by the evaluation levels of "low", "low", "average", "high", and "high".

**Table 7.** Numerical feature values of each indicator cloud model.


**Figure 4.** Per capita gross regional product cloud model.

**Figure 5.** Per capita fiscal revenue cloud model.

**Figure 6.** Retail sales of social goods per capita.

**Figure 7.** Cloud model of total expenditure ratio of per capita financial environmental protection expenditure.

**Figure 8.** Logistics industry as a percentage of fixed asset investment cloud model.

4.2.4. Calculate the Affiliation Degree of Each Index

After getting the cloud model of each evaluation index in the regional logistics low carbonization evaluation index system, we use the X conditional cloud generator of the cloud model by MATLAB programming and take N = 3000 to ge<sup>t</sup> the affiliation degree of different levels corresponding to each evaluation index of the province. According to the principle of maximum affiliation degree, select the level corresponding to the maximum of the affiliation degree as the index level, taking Beijing in 2013 as an example, show the results in Table 8.


**Table 8.** Indicator affiliation with Beijing 2013 as an example.

4.2.5. Entropy Weighting Method to Determine the Weights

Based on the entropy weighting method to calculate the weight of each index in the system, substitute the data of each index into the Equations (1)–(4) by MATLAB programming to calculate the weight of each index, and the results are shown in Table 9.


**Table 9.** Standardization of raw data for each indicator in Beijing.

4.2.6. Determine the Comprehensive Evaluation Level of Regional Logistics Index

First, the weights and affiliation degrees of each evaluation index are substituted into Equation (11) to obtain the comprehensive determination degree and evaluation grade, for example, the determination degree of each evaluation grade in Beijing in 2013 is *C* = (0.2204, 0.2689 , 0.3709 , 0.2260 , 0.0315 ). Second, according to the principle of maximum determination degree, select the evaluation grade with the maximum determination degree as the final comprehensive evaluation result, as shown in Tables 10–12. Finally, the Beijing-Tianjin-Hebei regional logistics decarbonization development grade from 2013 to 2019 do the comparison, as shown in Figure 9.

**Table 10.** Evaluation Results of Logistics Decarbonization Development in Beijing from 2013–2019.


**Table 11.** Evaluation Results of Logistics Decarbonization Development in Tianjin 2013–2019.



**Table 12.** Evaluation Results of Logistics Decarbonization Development in Hebei Province from 2013 to 2019.

**Figure 9.** 2013–2019 Beijing-Tianjin-Hebei Regional Logistics Decarbonization Development Grade Comparison.

From the time dimension of the comprehensive evaluation results, the overall development of logistics decarbonization in the Beijing-Tianjin-Hebei region from 2013 to 2019 is on an upward trend, with all three regions showing different degrees of improvement. Among them, the development of logistics decarbonization in Beijing develops from average level to high-level, that in Tianjin develops from lower level to average level, and that in Hebei develops from low-level to average level; relatively speaking, the development in Tianjin is slow, which is not in line with its economic level. From the spatial dimension of the comprehensive evaluation results, the development of logistics low-carbon within the Beijing-Tianjin-Hebei region is not balanced, the specific performance is Beijing Tianjin Hebei. There has been a level difference in the development of logistics decarbonization within the Beijing-Tianjin-Hebei region between 2013 and 2019, and the development to 2019, Beijing is at a high-level of development nationwide, while Tianjin and Hebei Province are still at an average level of development, which is two levels away from Beijing in the same region.

#### *4.3. Determination of Influencing Factors and Suggestions for Countermeasures*

#### 4.3.1. Determination of Influencing Factors

According to the development trend of each index and the horizontal comparison with the three provinces and cities in Beijing, Tianjin and Hebei, the shortcomings of each region in the development of logistics low carbonization are identified, and the main factors affecting the development of logistics low carbonization in the city are found, so as to provide theoretical support for the development of logistics low carbonization. Due to space limitations, the evaluation grade of each indicator is displayed in Beijing region as an example, as shown in Table 13; meanwhile, the evaluation grade of each indicator in Beijing, Tianjin and Hebei in 2019 is compared, as shown in Figure 10.


**Table 13.** Evaluation level of each indicator in Beijing region for example.

**Figure 10.** 2019 Beijing-Tianjin-Hebei comparison of evaluation ratings for each indicator.

From the evaluation grade of each indicator in Beijing, Tianjin and Hebei provinces and cities, the five indicators of per capita cargo turnover, the contribution rate of logistics industry to GDP, the part of logistics personnel in the workforce, the growth rate of logistics personnel and the growth rate of technical market turnover in Beijing are below the national average level all year, and the development is slow. By 2019, the efficiency of logistics industry, logistics industry input, logistics industry output and technical support are the indicators under the four secondary indicators are still below the national average level, and the shortcomings are more obvious. Tianjin region has been at a low or lower level nationally in the five indicators of per capita e-commerce sales, per capita cargo turnover, growth rate of new fixed asset investment in logistics industry, growth rate of technology market turnover, and growth rate of R&D funding during 2013–2019, and the development has been neglected, a large gap between the levels of the indicators under the low-carbon logistics environment support power and Beijing. Hebei Province has the most obvious gap in low-carbon logistics environment support power relative to neighboring Beijing and Tianjin, mainly in the form of per capita fiscal revenue per year at a low national level, per capita gross regional product and per capita total retail sales of social goods per year at a low-level. In addition, the three indicators of per capita e-commerce sales, per capita

turnover of goods and growth rate of technology market turnover in 2019 are still at a low or lower level nationwide.

As can be seen from Figure 10, the development of each indicator in Beijing, Tianjin and Hebei provinces and cities is still in an unbalanced state by 2019, with the biggest difference between Beijing and the other two provinces and cities, as shown because the evaluation levels of each indicator under the two secondary indicators of economic environment and logistics infrastructure are higher than those of Tianjin and Hebei, while the levels of each indicator in logistics industry efficiency and logistics industry input and output are significantly lower than those of the other two provinces and cities; Tianjin is generally higher than Hebei in the four secondary indicators of economic environment, policy environment, logistics infrastructure, and logistics industry scale, but not higher than Hebei in the indicators of low carbon logistics potential. Thus, it seems that although the three provinces and cities in Beijing, Tianjin and Hebei have made breakthroughs in cooperation, they still lack synergy in the development of low-carbon logistics due to the large differences in administrative division, consciousness and economic development level.

4.3.2. Suggestions for Countermeasures to the Low-Carbon Development of Logistics in Beijing-Tianjin-Hebei Region


and share and freely exchange logistics information so as to connect the information of each node of the supply chain and give full play to the advantages of regional informatization, to reduce logistics costs and improve logistics efficiency.
