Analysis of technological innovation on provincial green development levels of logistics industry in China

doi:10.21203/rs.3.rs-2196829/v1

Logistics industry is a crucial component in modern economy, but it also causes environmental damage. Technological innovation is an important measure to mitigate environmental degradation. To explore the impact of technological innovation on the provincial green logistics development level in China, this study utilizes a generalized estimating equations (GEE) regression model to analyze a panel data of 30 provinces during 2001-2019. Firstly, entropy method is applied to calculate the green logistics development level based on an index system considering green factors. Secondly, a GEE model which considers the correlation among different observations is used to investigate the impact of technological innovation, trade openness, government financial investment, logistics infrastructure, energy protection and energy intensity on green logistics development level. Moreover, regional heterogeneity is also analyzed in this paper by comparing the regression results of the Eastern region, Central region and Western region.Based on the above analysis, several conclusions are drawn: (1) Technological innovation positively affect green logistics development level, but its impact gradually decrease after a certain point. (2) Trade openness positively influence green logistics development level. (3) Logistics infrastructure positively affect green logistics development level. (4) Energy intensity of logistics industry negatively affect green logistics development level. Related policy recommendations are proposed based on our finding.

Technological innovation

Green logistics development level

Regional heterogeneity

Policy proposal

With a rapid development in recent years, logistics industry has gradually become an important component of Chinese national economy. Logistics industry is a composite service industry, which is recently developed based on an integration of various industries. Specifically, logistics industry consists of the express transportation that affects our daily life, energy transportation, cargo transportation and various activities generated in the transportation process. With the rapid development of both transportation and information networks related to railway, highway, aviation and other transportation forms, Chinese logistics industry has entered a stage of prosperous development. By the end of 2020, the total value of social logistics goods increased by 3.5% compared to the counterpart in 2019, and exceeded 301 trillion Yuan in China, indicating that the Chinese logistics market is the largest one in the world. In addition, the economic development mode has shifted from high-speed development to high-quality development in China since 2017. Consequently, in order to establish a large domestic market for logistics industry, Chinese government has promulgated several policies, laws and regulations to support the transition of traditional logistics industry, like “Opinions on Promoting High Quality Development of Logistics to Form Strong Domestic Market” promulgated by Chinese National Development and Reform Commission. In this situation, logistics industry has become another key driving force of Chinese economy. In short, logistics industry plays a crucial role in many economic aspects in China, such as promoting the transition and upgrading of Chinese industrial structure and enhancing the overall economic strength of China.

However, ecological environment has been damaged to a certain extent in China due to the rapid development of logistics industry, posing a threat to Chinese sustainable development. To be specific, plenty of air pollutant is emitted by logistics industry through both on-road mobile sources and non-road mobile sources. The CO₂ emissions produced by the transportation, storage, post and telecommunication services (TSPTS) industry was 732.48 million tons in 2019, ranking the 4th place in all sectors in China. The serious air pollution caused by logistics industry is mainly related to the increasing number of vehicles used for transportation. Other types of pollution caused by logistics industry include the overuse of resources, plastic pollution, waste and so on. Unreasonable storage layout and over packaging can lead to excessive consumption of land, steel, energy and other resources; meanwhile, non-biodegradable packaging materials and lack of recycling can result in land and water pollution. Moreover, unreasonable waste disposal in the circulation process can lead to environmental pollution. Take gross energy consumption as an example, the gross energy consumption of the TSPTS industry reached 439.09 million tons coal equivalent in 2019, accounting for approximate 9% of Chinese gross energy consumption. In summary, the logistic industry not only consumes plenty of resources, but also causes serious pollution and threat sustainable development.

Green and low-carbon development become an inevitable trend for Chinese logistic industry as much attention has been attached on sustainable development. Sustainable Development was formally proposed in 1987 as people began to recognize the potential hazards of resources overuses to future generations. How to achieve sustainable development has become one of the most important issues that we need to face and solve. Sustainable development is no longer a concept in the field of ecological environment, it affects many other fields like economic field and energy field. Chinese government has endeavored to achieve sustainable development since 1995 when sustainable development was perceived as a crucial development strategy. Later on, Chinese government has made many plans and promulgate several policies to facilitate sustainable development, like in the “Outlines of the 14th Five Year Plan”, Chinese government clearly mentioned that Chinse government will promote green development to realize the harmonious coexistence between human and nature, emphasizing the protection of the ecological environment and promotion of sustainable development. Regarding the logistics industry, Chinese government also provide several laws and policies aiming to promote the sustainable and green development, such as “Promotion law of Chinese Environmental Recycling Economy”, “Plan for the Development of a Modern Logistics System during the 14th Five Year” and “Comprehensive Working Scheme for Conserving Energy and Reducing Emissions during the 14th Five Year”. With the help of these polices, Chinese government hopes to establish a modern and green logistics system. In this context, the promotion of green development in logistics industry is an inevitable trend in China.

Technological innovation is a crucial factor for the development of green and low-carbon logistics industry (Zhang et al., 2020c). Specifically, technological innovation can not only improve the efficiency of different processes of logistics chain but also facilitate the transition progress of logistics industry. The improved efficiency is beneficial for the reduction of energy consumption, while the decrease of energy consumption leads to the decline of CO₂ emissions, thus, technological innovation can contribute to the development of green and low-carbon transition in logistics industry in China. The impact of technological innovation on logistics are also widely explored in previous literature (see examples in Long et al. (2020) and Fan et al. (2022)). However, previous studies concentrate on the impact of technologic innovation on the efficiencies of logistics industry. Studies which measure the green development levels of logistics industry in China is limited, let alone the impact of technological innovation on the green development levels of logistics industry.

In summary, the motivations of this paper are threefold: (1) Provincial green development levels of logistics industry in China are measured. (2) The impact of technological innovation on the green development levels of logistics industry in China is explored. (3) Related policies are proposed to promote the green development of logistics industry. To be specific, we firstly use entropy method to calculate the green development levels of logistics industry for 30 provinces based on an index system. Then, we find that technological innovation positively affects green logistics development level according to the regression results of the GEE model. Moreover, regional heterogeneity is also analyzed in our study. Finally, related policy recommendations are proposed based on our regression results.

The rest of the paper is organized as follows: Section 2 summarizes related literature. Section 3 evaluates the green development levels of logistics industry for 30 provinces in China. Section 4 is about the regression model and its results. Section 5 concludes this study.

There are two main branches in previous studies related to the factors affecting the development of logistics industry. To be specific, different studies conducted their research from the two perspectives: 1) logistics efficiency (LE) and 2) development levels of logistics industry (DLLI). Several recent papers are summarized in Table 1 and 2, respectively. Apart from these studies, other scholars pay attention to the issues related to CO₂ emissions of logistics industry, like the decoupling issues (Quan et al., 2020; Zhang et al., 2021) and influencing factors of CO₂ emissions (Guo and Wang, 2022).

With regards to the LE , many scholars utilized green total factor productivity (GTFP) or total factor productivity (TFP) as the indicator of LE, see examples in Zheng et al. (2020) and Li and Wang (2021). Few scholars applied other methods to calculate LE, like Yao et al. (2020) used a non-directional distance function method to estimate LE. Regarding the research contents, many scholars explored the impacts of different variables on LE, these factors included economic development, foreign direct investment, government regulation, technological innovation, human resources, industry agglomeration level and so on (details can be found in Table 1). The methods applied in these studies include spatial regression, Tobit regression, fixed-effect OLS, GMM, etc. Other scholars divided LE into technical progress and technical efficiency, and studied the evolution of LE in different regions, see Liu and Xu (2020) and Mao et al. (2022). Moreover, Zheng et al. (2021) investigated the influencing factors of the link efficiency between manufacturing industry and logistics industry. Lei et al. (2020) gauged the impact of LE on the employment structure for 49 listed logistics company in China.

For the strand of DLLI, scholars usually calculated DLLI based on an index system firstly. Generally, the index system considers the infrastructure and scale of logistics industry, economic development and information level (see examples in Tian and Zhang (2019) and Zhang et al. (2020b)). As attention has been paid on sustainable and low-carbon development in recent years, indicators related to environmental and green development of logistics industry are also included in some recent works (Fan et al., 2022; Zhang et al., 2020a). The common method used to calculate DLLI is entropy method, other methods included fuzzy-AHP, combined weights and ISM. After obtaining the DLLI, scholars may: 1) help company make decision by appraising the DLLI for different objectives (Ni et al., 2019; Zhang et al., 2020a); 2) explore the coupling relationship between logistics development, economic development and ecological development (Lan and Tseng, 2017; Zhang et al., 2020b); 3) investigated the impacts of other factors on logistics development (Fan et al., 2022; Lan et al., 2017; Sun, 2017; Tian and Zhang, 2019). These factors include economic development, urbanization, human resource, etc. Moreover, Xu and Wang (2017) studied the impacts of DLLI on economic development.

Table 1 Recent studies related to factors affecting logistics efficiency (LE)

No.	Authors	Indicators of LE	Methods	Research objective	Dependent variable	Influencing factors
1	Liang et al. (2019)	GTFP	SBM, ML indicator, dynamic GMM	13 cities in Jiangsu Province, 2006-2018	GTFP	AGG, ER, LEI, LP, GDP, TI
2	Tan et al. (2019)	GTFP	Super-SBM, Tobit	31provinces in China, 2007-2016	GTFP	GDP, LA, LQ, UR, URLR
3	Lei et al. (2020)	TFP	DEA, Malmquist indicator, FE OLS	49 listed companies in logistics industry, 2009-2017	EMST	TP (calculated in the TFP process), ENS, TRS, SS
4	Liu and Xu (2020)	GTFP	SBM, ML indicator	30 provinces in China, 2003-2017	This paper studied the evolution of GTFP, it also decomposed it into technical progress and technical efficiency.
5	Long et al. (2020)	GTFP	Super-SBM-DEA, SDM	11 provinces along Yangtze river, 2004-2016	GTFP	ER, FDI, GDP, GI, HC, LEI, IS
6	Yao et al. (2020)	GLE	Non-DDF, funHDDC	30 provinces in China, 2008-2017	This paper divide GLE into efficiency change (devoted by catch-up effect & innovation effect) and technology gap change.
7	Zheng et al. (2020)	GTFP	SBM-DEA, HR	17 provinces along the Belt and Road, 2007-2017	GTFP	GDP, GR, GS, LA, POP, TR
8	Guo et al. (2021)	GTFP	Super-SBM, Tobit	17 provinces along the Belt and Road, 2009-2018	GTFP	ER, FDI, GDP, IS, TI
9	Li and Wang (2021)	GTFP	EBM, ML indicator, GTWR	30 provinces in China, 2004-2017	GTFP	AGG, FDI, IL, LQ, TI, TS
10	Zheng et al. (2021)	GTFP	SBM-DEA, Tobit	3 provinces in northeast China, 2009-2016	This paper used Tobit model to analyze the impacts of GC, TCT and TR on LIE between manufacturing and logistics industry.
11	Bai et al. (2022)	GTFP	Super-SBM, SDM	30 provinces in China, 2005-2019	GTFP	ER, ES, FDI, GDP, LEI, IS, TI, UR
12	Li and Wang (2022)	GTFP	EBM, ML indicator, GWR	30 provinces in China, 2005-2017	GTFP	AGG, FDI, IL, IEI, TI, TND
13	Mao et al. (2022)	TFP	DEA, Malmquist indicator, ESDA	11 provinces along the Yangtze River, 2003-2017	This paper used ESDA model and Malmquist indicator to analyze the evolution of TFP in the 11 provinces.
Notes: 1. Abbreviations of methods: DEA, data envelopment analysis; EBM, epsilon-based measure; ESDA, exploratory spatial data analysis; FE OLS, fixed-effect ordinary least squares; funHDDC, functional high dimensional data clustering; GLE, green logistics efficiency; GMM, generalized method of moment; GTFP, green total factor productivity; GTWR, geographically and temporally weighted regression; GWR, geographically weighted regression; HR, hierarchical regression; LE, logistics efficiency; ML, Malmquist-Luenberger; non-DDF, non-directional distance function; SDM, spatial Durbin model; SBM, slack-based measurement; TFP, total factor productivity. 2. Abbreviations of influencing factors: AGG, agglomeration level; EMST, employment structure; ENS, enterprise size; ER, environmental regulation; ES, energy structure; FDI, foreign direct investment; GC, government consumption; GDP, gross domestic production; GI, government intervention; GR, government regulation; GS, government support; HC, human capital; IL, informatization level; IS, industry structure; LA, location advantage; LIE, linkage efficiency; LP, labour productivity; LQ, labour quality; LEI, energy intensity of logistics industry; POP, population; SS, salary structure; TCT, technical contract transaction amount; TI, technological innovation; TND, traffic network density; TR, trade openness; TRS, training structure; TS, transportation structure; UR, urbanization level; URLR, utilization rate of logistics resource.

Table 2 Recent studies related to factors affecting the development level of logistics industry (DLLI)

No.	Authors	Methods	Research objective	Dependent variable	Influencing factors
1	Lan and Tseng (2017)	IS without considering environmental indicators, entropy weight, CCD	Beijing, Shanghai, Guangzhou, Chongqing & Tianjin, 2009-2013	This paper studied the coupling relationship between economic development and logistics development.
2	Lan et al. (2017)	IS without considering environmental indicators, entropy weight, OLS	Beijing, Shanghai, Guangzhou, Chongqing & Tianjin, 2009-2013	DLLI	Economic development level calculated by using entropy method
3	Sun (2017)	IS without considering environmental indicators, combined weight, OLS	The relationship between urbanization & DLLI in Liaoning province, 2000-2015	DLLI	Urbanization levels calculated by using entropy method
4	Xu and Wang (2017)	IS without considering environmental indicators, entropy weight, SDM	26 cities along Yangtze River, 2005-2015	GDP	DLLI, GF, HR, LI, TR
5	Ni et al. (2019)	IS considering environmental indicators, fuzzy-AHP	Shandong Province	A green logistics index system related to agricultural products was proposed.
6	Tian and Zhang (2019)	IS without considering environmental indicators, entropy weight, SEM-FE	31 provinces in China, 2008-2016	DLLI	GDP, HR, IL, LI
7	Zhang et al. (2020b)	IS without considering environmental indicators, entropy weight, CCD	30 provinces in China, 2008-2017	This paper studied the coupling relationship among economic development, logistics development and ecological development.
8	Zhang et al. (2020a)	IS considering environmental indicators, DEMATEL, ISM	Logistics companies	A green logistics index system was proposed.
9	Fan et al. (2022)	IS considering environmental indicators, entropy weight, PVAR	4 provinces in Yangtze River Delta of China	The interaction among logistics industry and economy, energy and environment was analyzed by using PVAR.
Notes: 1. Abbreviations of methods: AHP, analytic hierarchy process; CCD, coupling coordination degree; DEMATEL, decision-making and trial evaluation laboratory; IS, index system; ISM, interpretation structure model; OLS, ordinary least squares; PVAR, panel vector autoregressive; SDM, spatial durbin model; SEM-FE, spatial error model with fixed effect. 2. Abbreviations of influencing factors: GF, government function; HR, human resource; IL, information level; LI, logistics infrastructure; TR, trade openness.

To sum up, although the studies related the development of logistics industry are rich, plenty of them concentrate on the analysis of the influencing factors of logistics efficiency. Studies which aim to investigate the impact of technological innovation on provincial green development levels in China is limited. Tian and Zhang (2019) utilized a spatial regression model to investigate the impacts of logistics infrastructure, economic development, information level and human resources on provincial logistics development levels in China. However, environmental and green indicators are neglected when they appraise the provincial logistics development level; moreover, they failed to explore the impact of technological innovation in their regression analysis. Therefore, to bridge the literature gap, we firstly propose an index system which contains indicators related to logistics infrastructure, logistics scale, economic development, information level and environmental level, and utilize an entropy method to evaluate the green logistics development level for 30 provinces during the period of 2001-2019. Then, we conduct a regression analysis to investigate the impact of technological innovation the provincial green logistics development level (GLDL). Their impacts of east region, central region and west region are also compared and analyzed. Related policies are proposed based on the results.

3.1 Evaluation index system of green logistics development level

The proposed evaluation index system of GLDL is shown in Table 3. Logistics infrastructure is the basis for the development of the whole industry; the scale of logistics is another crucial dimension; the economic development is the driving force for the development of the industry; informatization level is the driving force for the transformation of traditional logistics industry to green logistics industry; environmental level reflects the impact degree of logistics industry on environment. Therefore, 16 criteria are selected from these 5 aspects based on previous studies.

Table 3

Evaluation index system of *GLDL*
Dimensions	Criterion/factors	unit	Attribute	References
Logistics infrastructure	Number of logistics employees	10,000 people	+	Sun (2017), Tian and Zhang (2019), Xu and Wang (2017), Lan et al. (2017), Zhang et al. (2020b), Lan and Tseng (2017), Fan et al. (2022)
	Fixed asset investment in logistics industry	100 million RMB	+
	Highway mileage	10,000 km	+
	Railway mileage	10,000 km	+
Logistics scale	Number of express deliveries	10,000 pieces	+	Sun (2017), Tian and Zhang (2019), Xu and Wang (2017), Lan et al. (2017), Zhang et al. (2020b), Lan and Tseng (2017), Fan et al. (2022)
	Freight transportation	10,000 tons	+
	Freight turnover	100 million ton-km	+
	Value added of logistics industry	100 million RMB	+
Economic development	Urbanization level	%	+	Tian and Zhang (2019), Ni et al. (2019), Zhang et al. (2020a), Fan et al. (2022)
	Gross regional product	100 million RMB	+
	Per capita disposable income of urban residents	RMB	+
Information level	Investment in education	100 million RMB	+	Ni et al. (2019), Zhang et al. (2020a)
	Number of Internet users	10,000 people	+
	Scientific research expenditure	100 million RMB	+
Environment level	Energy consumption of logistics industry	10,000 tons of standard coal	-	Ni et al. (2019), Zhang et al. (2020a), Fan et al. (2022)
Environment level	CO₂ emissions of logistics industry	10,000 tons	-	Ni et al. (2019), Zhang et al. (2020a), Fan et al. (2022)

The original data of 30 provinces (Hongkong, Macao, Tibet and Taiwan are excluded due to the data accessibility) are mainly obtained from China Statistical Yearbook, China Energy Statistical Yearbook, China Science and Technology Statistical Yearbook and provincial statistical yearbooks from 2001 to 2019. Most of the data can be obtained directly from the yearbook, and a small part of the data needs to be calculated and converted. The collected data has been confirmed and checked for many times to ensure the accuracy, reliability and objectivity of the data.

3.2 Evaluation method

As the units of the original data is inconsistent, it is necessary to standardize the original data to overcome the influences of different dimensions and orders of magnitude, ensuring that the calculated results are authentic and reliable. Let $n$ denotes the numbers of sample, $T$ represents the time span of the observation period and $m$ denotes the number of indicators. In our case, $n$ equals to 30, $T$ is 19 years and $m$ equals to 16. Let ${x}_{ij}^{t}$ represents the value of the$j$-th index of sample $i$ in year $t$ ($i$=1, 2, … ,$n$; $t$ =1, 2, …,$T$; $j$=1, 2, …,$m$). There are 16 indexes in the proposed evaluation index system of green development level of logistics industry, including 14 positive indexes and 2 negative indexes. The standardized treatment of these indexes is as follows:

For positive indexes, Eq. (1) is used for standardization:

$${x}_{ij}^{t{\prime }}=\frac{{x}_{ij}^{t}-{min}\left({x}_{ij}^{t}\right)}{{max}\left({x}_{ij}^{t}\right)-{min}\left({x}_{ij}^{t}\right)}$$

1

For negative indexes, Eq. (2) is used in the standardization process:

$${x}_{ij}^{t{\prime }}=\frac{{max}\left({x}_{ij}^{t}\right)-{x}_{ij}^{t}}{{max}\left({x}_{ij}^{t}\right)-{min}\left({x}_{ij}^{t}\right)}$$

2

where $max\left({x}_{ij}^{t}\right)$represents the maximum value of the$j$-th index in the sample in year $t$. $min\left({x}_{ij}^{t}\right)$denotes the minimum value of the$j$-th index in the whole sample in year $t$. ${x}_{ij}^{t{\prime }}$ stands for the standardized value of the$j$-th index of sample $i$ in year $t$, and ${x}_{ij}^{t{\prime }}\in \left[\text{0,1}\right]$.

Based on the standardized data, this paper uses entropy weight method to determine the weight of each index, and calculates the values of green development level of logistics industry of each province in the sample years. The calculation process is shown as follows:

(1) Calculate the proportion of standardized value of each index:

$${y}_{ij}^{t}=\raisebox{1ex}{${x}_{ij}^{t{\prime }}$}\!\left/ \!\raisebox{-1ex}{${\sum }_{i=1}^{n}{x}_{ij}^{t{\prime }}$}\right.$$

3

where ${y}_{ij}^{t}$ represents the standardized proportion of the$j$-th index of sample$i$ in year $t$.

(2) Calculate the information entropy of each index:

$${e}_{j}^{t}=-\frac{1}{\text{ln}n}\sum _{i=1}^{n}{y}_{ij}^{t}\text{ln}{y}_{ij}^{t}$$

4

where ${e}_{j}^{t}$ denotes the entropy of the $j$-th index in year $t$.

(3) Calculate the difference coefficient of each index:

$${d}_{j}^{t}=1-{e}_{j}^{t}$$

5

where ${d}_{j}^{t}$ stands for the difference coefficient of the $j$-th index in year $t$.

(4) Calculate the weight of each index:

$${w}_{j}^{t}={d}_{j}^{t}/\sum _{j=1}^{m}{d}_{j}^{t}$$

6

where ${w}_{j}^{t}$ represents the weight of the $j$-th index in year $t$.

(5) Calculate the green logistics development level:

$${GLDL}_{i}^{t}=\sum _{j=1}^{m}{w}_{j}^{t}\ast {x}_{ij}^{t{\prime }}$$

7

where ${GLDL}_{i}^{t}$ represents the green logistics development level of $i$-th province in $t$-th year.

3.3 Spatial-temporal analysis of green logistics development level

The average GLDL for each province during 2001–2019 is listed in Table 4. As shown in Table 4, 8 of the top 10 provinces are located in the Eastern region, 2 of them are in the Central region and only 1 of them situate in the Western region. Moreover, the rank of the average GLDL of the Eastern region, Central region and Western region also indicates that development of green logistics in the Eastern region is faster than that of the other two regions. To be specific, solid economic foundation in the Eastern region contribute to the expansion of consumption market, which are beneficial for the expansion of logistics industry as logistics serve as a bridge that connect producers and consumers. Good economic foundation also promotes the application of information technology. Moreover, the flat terrain in the Eastern region also are conducive to the construction of logistics infrastructure. Therefore, the green logistics development in the Eastern region is fast. Similarly, the Central region not only connects the Eastern region and the Western region, but also link the North and South in China. Several provinces in the Central region are the centers of Chinese traffic network, like Henan, Anhui and Hunan province. The location advantage of the Central region is beneficial for its green logistics development. On the contrary, many of the provinces in the Western region have a vast territory with a sparse population, the economy scale and information level in these provinces are also relatively low. Consequently, the development level of green logistics in the Western region is low. However, owing to “Sustainable development” strategy and “One Belt and One Road” Initiative, the development level of green logistics in the Western region rises quickly in recent years.

Table 4

Green development level of logistics industry in some years of each province
Eastern region			Central region			Western region
Province	GLDL	Rank	Province	GLDL	Rank	Province	GLDL	Rank
Guangdong	0.7311	1	Henan	0.3799	7	Sichuan	0.3203	10
Shandong	0.5458	2	Anhui	0.3211	9	Inner Mongolia	0.2299	16
Jiangsu	0.5301	3	Hunan	0.2991	11	Shaanxi	0.2119	18
Zhejiang	0.5213	4	Hubei	0.2843	14	Guangxi	0.2190	17
Shanghai	0.4875	5	Shanxi	0.2025	19	Yunnan	0.1891	22
Hebei	0.3845	6	Jiangxi	0.2011	20	Chongqing	0.1800	23
Beijing	0.3464	8	Heilongjiang	0.1983	21	Xinjiang	0.1438	25
Fujian	0.2951	12	Jilin	0.1672	24	Guizhou	0.1253	26
Liaoning	0.2861	13				Gansu	0.1102	27
Tianjin	0.2435	15				Ningxia	0.0628	29
Hainan	0.0703	28				Qinghai	0.0611	30
Eastern	0.4038		Central	0.2567		Western	0.1685

The spatial distribution of GLDL of different provinces in 2001, 2005, 2008, 2012, 2015 and 2019 are shown in Fig. 1. It can be observed from Fig. 1 that the GLDL has a significant decreasing trending from the Eastern region to the Western region. Moreover, the GLDL of the coastal provinces is also obviously higher than that of the inland provinces. In other words, the spatial distribution of the GLDL confirms the ranks of the average GLDL of different provinces during 2001–2019. Guangdong ranks the 1st place in terms of GLDL as it has a good economic foundation and logistics infrastructure. Moreover, Guangdong is also an important manufacturing base and a crucial trade base in China. Its environment also improves recently due to the continuous effects of the its government and enterprises. Therefore, the GLDL is high in Guangdong. On the other hand, Qinghai is an inland province with weak economic performance and fragile ecological environment. Logistics infrastructure in Qinghai is weak due to the fragile environment. The scale of logistics industry is also small due to the weak logistics infrastructure and economy. As a result, the development level of green logistics in Qinghai is low.

4.1 Econometric model

Technological innovation is a crucial factor affecting development level of green logistics. Specifically, research and development (R&D) can yield technologies or products which can improve the energy efficiency or operation efficiency in logistics industry, such as electronic motor car with long endurance mileage, goods sorting system, internet of things and so on. Promotion of such technology and high-tech productors are beneficial for the development of green logistics. Moreover, technological innovation can also change the future development direction of the industry. With the development of green development of logistics industry in China, companies in logistics industry tend to utilize green technology. In summary, technological innovation and R&D can effectively promote the development of the industry and enhance competitiveness. Referring to Li and Wang (2022) and Shen et al. (2019), this paper uses the R&D expenditure of high-tech industry (TI) to express the innovation ability.

Trade openness (TR) is an important influencing factor for GLDL. To be specific, the promotion of international trade can contribute to the economic development, most of the countries which actively involve in international trade have a vigorous economy. The promotion of international trade can drive the circulation of goods and service and facilitate the development of logistics industry and its related industries. With reference of Khan et al. (2019) and Zheng et al. (2020), this paper uses the total values of import and export to denotes TR.

Government regulation is also an important factor for GLDL. In China, the government mainly regulates market order of logistics industry through macro-control such as financial investment in transportation section. The application of government regulation may facilitate the development of logistics infrastructure, optimize resources allocation of different links in logistics industry, reduce related costs and increase efficiency, and improve ecological environment. However, it may also impede the development of green logistics as too much government financial investment may hinder the investment from companies, which may reduce the market efficiency. Moreover, if the financial investment is used to infrastructure construction with high pollutions, the development of green logistics would also be damaged. With reference to Long et al. (2020) and Xu and Xu (2022), this paper utilizes government financial input in transportation industry (GFI) to represent government regulation.

Infrastructure construction is also an important factor affecting GLDL. Logistics infrastructure not only involves warehousing, computers, transportation equipment, but also involves road, railway and other transportation infrastructure. In summary, infrastructure construction lays the foundation for the development of green logistics. Unlike Tan et al. (2019) and lo Storto and Evangelista (2022) who used the total mileage of different transportation manners to represent logistics infrastructure (LF), we use the area of logistics storage land to represent the LF.

Environmental protection (EP) is an important factor which have impact on the GLDL. The attitude of logistics industry towards energy protection can be partly reflected by the governance capability and the construction of governance system related to environment in logistics industry. To be specific, the utilization efficiency of resources can be improved by enhancing the EP of logistics industry, which are conductive to the long-term and sustainable development of green logistics. In order to explore the impact of EP on GLDL, we use proportion of investment in environmental pollution control to GDP to denote the EP (see other examples in Deng et al. (2020) and Wang et al. (2022)).

Energy intensity (EI) is also a crucial factor affecting the GLDL. As the market demand of logistics industry is expanding, its energy consumption is increasing accordingly, especially the energy consumption of the transportation process. Although the government has implemented a plenty of policies and measures to control energy consumption and pollution emissions, these policies still cannot resist the continuous growth of energy consumption, which jeopardizes the green development of logistics industry. With reference to Bai et al. (2022) and Long et al. (2020), this paper uses the proportion of energy consumption to the added value of logistics industry to express EI.

Although the main goal of this paper is to explore the impacts of TI on GLDL, other control variables are also considered based on the analysis above, such control factors include TR, GFI, LF, EP and EI. The relationship between GLDL and these independent variables can be described by the Eq. (8):

$${GLDL}_{it}=f\left({TI}_{it}, {TR}_{it},{GFI}_{it},{LF}_{it},{EP}_{it},{EI}_{it}\right)$$

8

where i and t denotes i-th province and t-th year respectively. GLDL represents green logistics development level, TI denotes technological innovation, TR stands for trade openness, GFI represents government financial investment, LF denotes logistics infrastructure, EP stands for environmental protection and EI denotes energy intensity.

In order to avoid the possible impact of data fluctuation, variance of units of different variables and heteroscedasticity, we use the logarithm form of the original data in our analysis. Thus, the regression model in our paper is shown in Eq. (9)

$${lnGLDL}_{it}=c+{\beta }_{1}{TI}_{it}+{\beta }_{2}{TR}_{it}+{\beta }_{3}{GFI}_{it}+{\beta }_{4}{LF}_{it}+{\beta }_{5}{EP}_{it}+{\beta }_{6}{EI}_{it}+{\epsilon }_{it}$$

9

where $i$is the provinces ($i$=1,2,3…,30), and $t$ is the year ($t$=2001,2002…,2019). ${\beta }_{1}$-${\beta }_{6}$ is the regression coefficient of these variables. ${\epsilon }_{it}$ is a random error term.

4.2 Data

In order to explore the impact of TI on GLDL, this paper collects data for 30 provinces (Tibet, Hong Kong, Macao and Taiwan are excluded due to the lack of data) during the period 2001 to 2019. The data used in this study is collected and sorted from the China Statistical Yearbook, China Financial Yearbook, China Science and Technology Yearbook, etc. For some missing data in particular year, average interpolation method is adopted to calculate the missing data. The definition of each variables and its descriptive statistics are listed in Table 5.

Table 5

Variables and descriptive statistics
Variables	Interpretation	N	Mean	S.D.	Min	Max
lnGLDL	Green logistics development level	570	-1.46	0.639	-2.99	-0.21
lnTI	R&D of high-tech industry	570	11.59	2.10	4.14	16.30
lnTR	Total trade value of import and export	570	5.49	1.79	0.68	9.30
lnGFI	Government financial input in transportation industry	570	4.04	1.86	-1.19	7.59
lnLF	Land area for logistical storage	570	3.59	0.79	0.69	5.74
lnEP	Proportion of investment in environmental pollution control to GDP	570	-2.78	0.50	-4.38	-1.35
lnEI	Ratio of total energy consumption to added value of logistics industry	570	0.13	0.48	-1.73	1.14
Note: S.D. denotes standard deviation, Min represents minimum, Max represents maximum

4.3 Pre-benchmark regression test

4.3.1 LM test and CD test

As cross-sectional dependence may exist in panel data, and it could result in biased regression results. Therefore, we conduct LM test and CD test to check whether there exists a cross-sectional dependence in our data. Results of the two classical cross-sectional dependence tests is listed in Table 6, and it implies that cross-sectional dependence occurs in our sample as the hull hypothesis (there is no cross-sectional dependence in the sample) is rejected. In order to solve the cross-sectional dependence issues, we apply a GEE model in our benchmark regression.

Table 6

Results of the cross-sectional dependence tests
Tests	Statistics	Prob.
Breusch-Pagan LM test	1618.77^***	0.0000
Pesaran CD test	19.746 ^***	0.0000
Note: ^*** denotes significance at the 1% level.

4.3.1 Unit root test

Before conducting a panel data regression, we should ensure that the data is stable. If the data is unstable, it is easy to yield bad regression results which affects the accuracy and consistency of regression results. Therefore, we conduct unit root test firstly. As the data is cross-dependent, traditional first-generation unit root tests are not suitable. Therefore, we utilized 2 second-generation unit root in our paper, i.e. Pesaran CADF test and Pesaran CIPS test. The results are listed in Table 7. As shown in Table 7, these variables are stable after first difference.

Table 7

Results of the Pesaran CADF and CIPS tests.
Variables	Level	1st difference	Order of integration
Variables	Intercept	Intercept	Order of integration
Pesaran CADF test
lnGLDL	-1.212	-2.763^***	I(1)
lnTI	-2.186^***	-3.250^***	I(1)
lnTR	-1.855	-2.578^***	I(1)
lnGFI	-3.211^***	-4.311^***	I(1)
lnLF	-2.153^**	-3.142^***	I(1)
lnEP	-2.546^***	-3.308^***	I(1)
lnEI	-2.441^***	-3.460^***	I(1)
Pesaran CIPS test
lnGLDL	-1.199	-4.229^***	I(1)
lnTI	-2.491^***	-4.580^***	I(1)
lnTR	-1.718	-3.107^***	I(1)
lnGFI	-2.651^***	-4.144^***	I(1)
lnLF	-2.640^***	-4.359^***	I(1)
lnEP	-2.256^**	-4.137^***	I(1)
lnEI	-2.312^**	-4.486^***	I(1)
Note: *, , and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively

4.4 Results and discussion

4.4.1 National regression analysis results

As the conditions in different provinces vary greatly in China, we apply a GEE model which is proposed by Zeger et al. (1988). Compared with traditional ordinary least squares (OLS) regression model, the distinction among different provinces and the changes over different period of each province are considered in the GEE model. In other words, the correlation between observations are considered in the GEE model.

Regression results of the fixed-effect (FE) OLS, feasible generalized least squares (FGLS) and GEE model is shown in Table 8. The result of GEE model is basically consistent with that of FGLS, and is different from that of FE-OLS, which confirms that the result of OLS is biased. According to the regression result of the GEE model in Table 8, we can conclude that TI has a positive impact on GLDL. Specifically, 1% increase in R&D can yield a 0.056% growth in GLDL. With regards to the control variables, TR and LF have a positive impact on GLDL, while GFI and EI have a negative impact on GLDL.

Table 8

Regression results of full sample
Dependent variable: lnGDLD
Variables	Regression method
Variables	FE	FGLS	GEE
lnTI	0.013	0.058***	0.056***
	(1.38)	(41.13)	(5.08)
lnTR	0.040***	0.157***	0.155***
	(3.08)	(68.04)	(12.33)
lnGFI	-0.006	-0.107***	-0.110***
	(-0.71)	(-40.17)	(-12.14)
lnLF	-0.005	0.394***	0.408***
	(-0.17)	(65.17)	(20.38)
lnEP	0.149***	0.015	-0.006
	(2.93)	(1.22)	(-0.09)
lnEI	-0.081*	-0.095***	-0.086*
	(-1.67)	(-8.80)	(-1.66)
Constant	-1.366***	-3.924***	-3.985***
	(-9.05)	(-117.18)	(-26.53)
Observations	570	570	570
R-squared	0.042
Number of id	30	30	30
Note: t-statistics in parentheses * p < 0.01, p < 0.05, * p < 0.1

As shown in Table 8, TI has a significant positive impact on GLDL. The result implies that the green development of logistics industry depends on the development of science and technology. R&D investment continuously keeps increasing in recent years, and the ever-increasing R&D sever as an important source for the high-quality development and promote the evolution of logistics industry. Abundant R&D funds and good research environment have yielded many research outputs which can be applied in many aspects related to logistics industry. Such research outputs include improvement and innovation of transportation equipment, invention of eco-friendly materials of packaging, etc. In summary, an increase in R&D investment have a positive effect on the green development of logistics industry in China.

As displayed in Table 8, TR has a positive effect on GLDL. China is an important production base in the world, and international trade is a crucial component in China’s economy. An increase in the total value of imports and exports can significantly stimulate economic development in Chia. As an important bridge linking both domestic and international market, the green development of logistics will also accelerate with continuous increase in economy. Therefore, TR positively affects GLDL.

As shown in Table 8, GFI has a significant negative impact on GLDL. When government commits large financial investment in transportation section, investment chance for the investors from the private sector is occupied. According to classical economic theory, this may cause decrease in market efficiency. Consequently, the development of green logistics may be hindered. Moreover, as the government financial investment is mainly used to build highways, railways or airport, which may cause pollutions as many steels and energy are needed in the construction process. Therefore, an increase in GFI negatively influence the GLDL.

As shown in Table 8, LF has a significant positive impact on GLDL. Logistic infrastructure, which is denoted by land area for logistical storage in this paper, lays a solid foundation for the development of logistics industry. An increase in LF implies the foundation of logistics industry becomes more solid; thus, the GLDL increases accordingly.

As shown in Table 8, EP has an insignificant negative impact on GLDL. Although the government's investment in environmental control has increased year by year, the growth rate of environmental protection cannot keep up with the growth rate of GDP, this will lead to imbalance between economic development and environmental control. Therefore, EP may negatively affect GLDL, but its impact is not significant.

As shown in Table 8, EI has a significant positive impact on GLDL. Energy intensity of logistics industry represents the utilization efficiency of logistics industry. Currently, traditional fossil fuels which result in environmental pollutions are still widely applied as long-distance vehicles using clean energy are still in the design and test process. An increase in EI indicates that more energy is needed to yield the same GDP, this will cause damage to the current environment and impede the green development of logistics industry.

4.4.2 Regional regression analysis results

As the conditions in different regions varies greatly in China, we conduct a heterogeneity analysis in which 30 provinces are divided into Eastern region, Central region and Western region in this study. The heterogeneity analysis can help us gauge the impacts of each variable on the GLDL in the three different regions; then we can propose different policies based on the regression results.

The results are listed Table 9.

Table 9

Regression results of different regions
	(1)	(2)	(3)	(4)
Variables	Total	Eastern region	Central region	Western region
lnTI	0.056***	0.043	0.115***	0.055***
	(5.08)	(1.02)	(4.22)	(4.05)
lnTR	0.155***	0.134***	-0.017	0.180***
	(12.33)	(2.64)	(-0.28)	(6.86)
lnGFI	-0.110***	-0.114***	-0.051	-0.112***
	(-12.14)	(-7.86)	(-1.64)	(-7.13)
lnLF	0.408***	0.433***	0.250***	0.427***
	(20.38)	(13.02)	(4.14)	(8.74)
lnEP	-0.006	-0.174*	0.030	0.000
	(-0.09)	(-1.85)	(0.18)	(0.00)
lnEI	-0.086*	0.041	-0.312**	0.147
	(-1.66)	(0.49)	(-2.47)	(1.52)
Constant	-3.985***	-4.255***	-3.324***	-4.191***
	(-26.53)	(-17.43)	(-9.59)	(-15.56)
Observations	570	209	152	209
Number of id	30	11	8	11
Notes: z-statistics in parentheses * p < 0.01, p < 0.05, * p < 0.1

For the Eastern region, TR and LF have a significantly positive impact on GLDL, while GFI and EP exert a negatively influence on GLDL. With regards to the Central region, TI and LF have a significantly positive impact on GLDL, while EI exerts a negatively influence on GLDL. Regarding the Western region, TI, TR and LF have a significantly positive impact on GLDL, while GFI exerts a negatively influence on GLDL.

For TI, the impact of TI on GLDL is not significant for the Eastern region. This may be caused by scale effect of R&D, i.e., the impact of R&D investment on GLDL will gradually increase when the technological level is low; however, the influence of R&D investment will become small when the technological level exceeds a turning point. To be specific, when the technological level is low, it is easy to realize technological innovation as it is relatively easy to invent an early-stage technology. Meanwhile, with continuous development of the early-stage technology, it will become more and more difficult to modify the sophisticated technology as more resources are needed. This is partly confirmed by the values and significance of coefficients of TI in the three regions: the coefficient of TI in the Central region is higher than that of TI in the Western region, meanwhile, the coefficient of TI in the Eastern region is insignificant.

With regards to TR, the impact of TR on GLDL is insignificant in the Central region. International trade is prosperous due to “One Belt and One Road” Initiative in the Eastern region and Western region due to their good location advantage. The volume of international trade in the Central region is small. Therefore, the impact of TR on GLDL is small in the Central region. Moreover, many heavy industries with high pollution are situated in the Central region, an increase in TR in the Central region will result in severe environmental damage, which will hinder the green development of logistics in the Central region. Consequently, the sign of TR in the Central region is negative.

Regarding GFI, the impact of GFI on GLDL is not significant in the Central region. GFI in the Central region is less than that in the Eastern and Western regions. Thus, the crowding-out effect of government investment in the Central region is the smallest in the three regions, indicating that the impact of GFI on GLDL is small in the Central region.

For LF, the impact of LF on GLDL is significant in these three regions, but the coefficient of LF in the Central region is smaller than the counterparts in the Eastern and Western regions. In other words, a U-shaped relationship exist between LF and GLDL, i.e., the impact of land area of logistical storage on the GLDL will decrease firstly, then started to increase after exceeding a turning point.

With regards to EP, EP has a significant negative impact on GLDL in the Eastern region. As mentioned in 4.4.1, the gap between the growth rate of GDP and environmental control input causes the negative relationship between EP and GLDL. Moreover, as the economic development in the Eastern region is better than that of the Central region and the Western region, this kind of gap is more significant in the Eastern region; therefore, EP exerts a significantly negative impact on GLDL in the Eastern region.

Regarding EI, the coefficient of EI is only significant in Central region. Several provinces in the Central region is an important energy base and heavy industry base for China, such as Shanxi, Heilongjiang and Jilin. In other words, many fossil energy production centers or high-pollution industries locate in the Central provinces. In contrast, many renewable energy projects or high-tech industries are situated in the Eastern or Western region. Therefore, EI has a significant negative impact on GLDL in the Central region.

4.5 Robustness test

Two kinds of robustness test are conducted in our study. Firstly, we compare the regression results of different regression model. The comparison result is listed in Table 8. As displayed in Table 8, the results of FE-OLS, FGLS and GEE are basically consistent. Secondly, we use number of patents (NP) to denote technological innovation instead of R&D in the GEE estimator. The results are listed in Table 10. As the two regression results are consistent, we can conclude that our result is robust.

Table 10

Robustness test
	(1)	(2)
Variables	R&D	NP
lnTI	0.056***
	(5.08)
lnNP		0.149***
		(6.58)
lnTR	0.155***	0.124***
	(12.33)	(8.59)
lnGFI	-0.110***	-0.139***
	(-12.14)	(-13.98)
lnLF	0.408***	0.367***
	(20.38)	(16.87)
lnEP	-0.006	0.096
	(-0.09)	(1.47)
lnEI	-0.086*	-0.132**
	(-1.66)	(-2.54)
Constant	-3.985***	-3.947***
	(-26.53)	(-26.87)
Observations	570	570
Number of id	30	30
Note: z-statistics in parentheses, * p < 0.01, p < 0.05, * p < 0.1

To explore the impact of technological innovation on the provincial green logistics development level in China, this study utilizes a GEE regression model to analyze a panel data of 30 provinces during 2001–2019. Firstly, entropy method is applied in this paper to calculate the green logistics development level based on an index system considering green factors. Secondly, a GEE model which considers the correlation among different observations is used to investigate the impact of technological innovation, trade openness, government financial investment, logistics infrastructure, energy protection and energy intensity on green logistics development level. Moreover, region heterogeneity is also analyzed in this paper by comparing the regression results of the Eastern region, Central region and Western region. Based on the above analysis, several conclusions and related policy recommendations are proposed below:

(1) Technological innovation, trade openness and logistics infrastructure positively affect green logistics development level from the perspective of full sample; meanwhile, government financial investment in transportation industry and energy intensity of logistics industry negatively influence green logistics development level.

(2) For the Eastern region, trade openness and logistics infrastructure positively affect green logistics development level; while government financial investment in the transportation industry and environmental protection input have negative impact on green logistics development level. Therefore, in order to keep high green logistic development level, provinces in the Eastern regions should: (i) upgrade manufacturing industry. China, especially the Eastern region of China, is an important production base in the world. By upgrading manufacturing industry, products with high-technology are produced and exported, energy intensity will also reduce. These are beneficial for the green development of logistics industry. (ii) reduce government financial investment in transportation sector. The government in the Eastern regions can utilize other measures to encourage private investors to participate in the transportation investment, like public private partnership (PPP). (iii) enhance environmental control expenditure in transportation sector. The increase rate of environmental protection expenditure lags behind the growth rate of economic development. Therefore, economic development is achieved at the cost of environmental damage. In order to modify this situation, more environmental protection investment should be committed.

(3) For the Central region, technological innovation and logistics infrastructure positively affect green logistics development level; while energy intensity of logistics industry negatively influences green logistics development level. Therefore,to keep high green logistic development level, provinces in the Central regions should: (i) maintain R&D investment as technological innovation are conducive for green development of logistics industry. (ii) modify energy supply and consumption structure. Many provinces in the Central region rely on energy production and heavy industry, these could hinder the green development of logistics; therefore, they could try to transform their energy structure to a green one. (iii) actively participate in international trade as international trade is proven to be beneficial for green development of logistics industry.

(4) For the Western region, technological innovation, trade openness and logistics infrastructure positively affect green logistics development level; while government financial investment in the transportation industry negatively affect green logistics development level. Therefore, to keep high green logistic development level, provinces in the Western regions should: (i) increase R&D investment as technological innovation contribute to green development of logistics industry. (ii) encourage international trade by taking the opportunity of “One Belt and One Road” Initiative as international trade are beneficial for green development of logistics industry. (iii) reduce government financial investment in transportation sector. The government in the Western regions can also encourage private investors to take part in construction of logistics infrastructure by providing PPP projects, build-own-transfer projects.

Although we have explored the impact of technological innovation on the green logistics development level in this paper, there are still some limitations. Firstly, a linear relationship between technological innovation and green logistics development level is assumed in our model. However, according to the regional heterogeneity analysis, the relationship between them may not be linear. Therefore, we will further study their relationship in our future research. Secondly, the mechanism how technological innovation affect green logistics development level is not fully investigated in our study, we will explore the impact mechanism in our future study.

DLLI	development levels of logistics industry
EI	energy intensity
EP	environmental protection
FE	fixed effect
FGLS	feasible generalized least squares
GEE	generalized estimating equations
GFI	government financial input in transportation industry
GLDL	green logistics development level
GTFP	green total factor productivity
LE	logistics efficiency
LF	logistics infrastructure
NP	number of patents
OLS	ordinary least squares
R&D	research and development
TFP	total factor productivity
TI	technological innovation
TR	trade openness
TSPTS	transportation, storage, post and telecommunication services

Availability of data and materials Most of the basic data are publicly available, mainly from China Statistical Yearbook, China Energy Statistical Yearbook, China Science and Technology Statistical Yearbook and provincial statistical yearbooks. Other data are calculated by authors, and the calculation method is shown in the text of this paper.

Author contributions Cheng Cheng: Conceptualization, formal analysis, methodology, writing-review and editing and visualization. Yanan Han: Investigation, formal analysis, validation and data curation and software. Xiaohang Ren: Formal analysis, investigation, resources, project administration and visualization.

Funding The article is supported by the National Natural Science Foundation of China (Grant No. 71904111, 71774105). Certainly, all remaining errors are our own.

Declarations

Ethics approval Not applicable.

Consent to participate Not applicable.

Consent for publication Not applicable.

Competing interests The authors declare no competing interests.

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Analysis of technological innovation on provincial green development levels of logistics industry in China

Status:

Journal Publication

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Abstract

Figures

1. Introduction

2. Literature Review

3. Evaluation Of Green Logistics Development Level In China