Dynamic evolution trend of comprehensive transportation green efficiency in China: From a spatio-temporal interaction perspective

doi:10.1007/s11442-022-1957-x

摘要/Abstract

Abstract:

It is urgent and important to explore the dynamic evolution in comprehensive transportation green efficiency (CTGE) in the context of green development. We constructed a social development index that reflects the social benefits of transportation services, and incorporated it into the comprehensive transportation efficiency evaluation framework as an expected output. Based on the panel data of 30 regions in China from 2003-2018, the CTGE in China was measured using the slacks-based measure-data envelopment analysis (SBM-DEA) model. Further, the dynamic evolution trends of CTGE were determined using the spatial Markov model and exploratory spatio-temporal data analysis (ESTDA) technique from a spatio-temporal perspective. The results showed that the CTGE shows a U-shaped change trend but with an overall low level and significant regional differences. The state transition of CTGE has a strong spatial dependence, and there exists the phenomenon of “club convergence”. Neighbourhood background has a significant impact on the CTGE transition types, and the spatial spillover effect is pronounced. The CTGE has an obvious positive correlation and spatial agglomeration characteristics. The geometric characteristics of the LISA time path show that the evolution process of local spatial structure and local spatial dependence of China’s CTGE is stable, but the integration of spatial evolution is weak. The spatio-temporal transition results of LISA indicate that the CTGE has obvious transfer inertness and has certain path-dependence and spatial locking characteristics, which will become the major difficulty in improving the CTGE.

Key words: comprehensive transportation green efficiency, spatio-temporal interaction, dynamic evolution trend, spatial markov model, exploratory spatio-temporal data analysis

. [J]. 地理学报(英文版), 2022, 32(3): 477-498.

MA Qifei, JIA Peng, SUN Caizhi, KUANG Haibo. Dynamic evolution trend of comprehensive transportation green efficiency in China: From a spatio-temporal interaction perspective[J]. Journal of Geographical Sciences, 2022, 32(3): 477-498.

图/表 13

参考文献 54

[1]	Anderson S C, Fielding G L, 1982. Comparative analysis of transit performance. Final Report for the US Department of Transportation, Washigton, DC.
[2]	Belmonte-Urea L J, Plaza-Beda J A, Vazquez-Brust D et al., 2021. Circular economy, degrowth and green growth as pathways for research on sustainable development goals: A global analysis and future agenda. Ecological Economics, 185(19), 107050: 1-17.
[3]	Cao X D, Wang J J, Chen C C, 2021. Efficiency of traffic structure based on SBM-Tobit-GWR model. Journal of Southwest Jiaotong University, 56(3): 594-601. (in Chinese)
[4]	Chang Y T, Zhang N, 2017. Environmental efficiency of transportation sectors in China and Korea. Maritime Economics & Logistics, 19(1): 68-93.
[5]	Charnes A, Cooper W, Roodes E, 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6): 429-444. doi: 10.1016/0377-2217(78)90138-8
[6]	Chen Y F, Cheng S Y, Zhu Z T, 2021. Measuring environmental-adjusted dynamic energy efficiency of China’s transportation sector: A four-stage NDDF-DEA approach. Energy Efficiency, 14(3): 1-14. doi: 10.1007/s12053-020-09910-3
[7]	Cheng Y, Lv K J, Wang J et al., 2019. Energy efficiency, carbon dioxide emission efficiency, and related abatement costs in regional China: A synthesis of input-output analysis and DEA. Energy Efficiency, 12(4): 863-877. doi: 10.1007/s12053-018-9695-8
[8]	Cooper W W, Seiford L M, Tone K, 2007. Data Envelopment Analysis. 2nd ed. Boston: Kluwer Academic Publishers.
[9]	Cui Q, Li Y, 2014. The evaluation of transportation energy efficiency: An application of three-stage virtual frontier DEA. Transportation Research Part D: Transport and Environment, 29: 1-11. doi: 10.1016/j.trd.2014.03.007
[10]	Daraio C, Diana M, Costa F D et al., 2016. Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research. European Journal of Operational Research, 248(1): 1-20. doi: 10.1016/j.ejor.2015.05.059
[11]	Fielding G J, Babitsky T T, Brenner M E, 1985. Performance evaluation for bus transit. Transportation Research Part A: Policy and Practice, 19(1): 73-82. doi: 10.1016/0191-2607(85)90009-3
[12]	Fitzová H, Matulová M, Tomeš Z, 2018. Determinants of urban public transport efficiency: Case study of the Czech Republic. European Transport Research Review, 10(2), 42: 1-11. doi: 10.1007/s12544-017-0273-5
[13]	Guan W., Xu S.T., 2016. Study of spatial patterns and spatial effects of energy eco-efficiency in China. Journal of Geographical Sciences, 26(9): 1362-1367. doi: 10.1007/s11442-016-1332-x
[14]	Jia P, Hu Y, Yuan S et al., 2020. A research on the comprehensive transportation efficiency and its spatial relevancy in China’s provinces. Science Research Management, 41(9): 219-229. (in Chinese)
[15]	Jiang X H, Ma J X, Zhu H Z et al., 2020. Evaluating the carbon emissions efficiency of the logistics industry based on a super-SBM model and the Malmquist index from a strong transportation strategy perspective in China. International Journal of Environmental Research and Public Health, 17(22), 8459: 1-19. doi: 10.3390/ijerph17010001
[16]	Levinson D, 2003. Perspectives on efficiency in transportation. International Journal of Transport Management, 1(3): 145-155. doi: 10.1016/j.ijtm.2004.01.002
[17]	Li Y, Du Q, Lu X R et al., 2019. Relationship between the development and CO₂ emissions of transport sector in China. Transportation Research Part D: Transport and Environment, 74: 1-14. doi: 10.1016/j.trd.2019.07.011
[18]	Liu B Q, Wu W, Su Q et al., 2018. Spatio-temporal evolution characteristics and mechanism of railway efficiency in China. Geographical Research, 37(3): 512-526. (in Chinese)
[19]	Liu H W, Wu J, Chu J F, 2018. Environmental efficiency and technological progress of transportation industry-based on large scale data. Technological Forecasting and Social Change, 144: 475-482. doi: 10.1016/j.techfore.2018.02.005
[20]	Liu H W, Yang R L, Wang Y Q et al., 2020. Measuring performance of road transportation industry in China in terms of integrated environmental efficiency in view of streaming data. Science of the Total Environment, 727, 138675: 1-13.
[21]	Ma Q F, Jia P, Kuang H B, 2021. Green efficiency changes of comprehensive transportation in China: Technological change or technical efficiency change? Journal of Cleaner Production, 304, 127115: 1-11.
[22]	Mangesh V L, Tamizhdurai P, Krishnan P S et al., 2020. Green energy: Hydroprocessing waste polypropylene to produce transport fuel. Journal of Cleaner Production, 276, 124200: 1-16.
[23]	Michaelides P G, Belegri-Roboli A, Marinos T, 2010. Evaluating the technical efficiency of trolley buses in Athens, Greece. Journal of Public Transportation, 13(4): 93-109.
[24]	Omrani H, Shafaat K, Alizadeh A, 2019. Integrated data envelopment analysis and cooperative game for evaluating energy efficiency of transport sector: A case of Iran. Annals of Operations Research, 274(1): 471-499. doi: 10.1007/s10479-018-2803-5
[25]	Oum T, Yu C, 1994. Economic efficiency of railways and implications for public policy: A comparative study of the OECD countries’ railways. Journal of Transport Economics and Policy, 28(2): 121-138.
[26]	Palander T, Haavikko H, Kortelainen E et al., 2020. Comparison of energy efficiency indicators of road transportation for modeling environmental sustainability in “Green” circular industry. Sustainability, 12(7), 2740: 1-22. doi: 10.3390/su12010001
[27]	Park Y S, Lim S H, Egilmez G et al., 2016. Environmental efficiency assessment of U.S. transport sector: A slack-based data envelopment analysis approach. Transportation Research Part D: Transport and Environment, 61: 152-164. doi: 10.1016/j.trd.2016.09.009
[28]	Quah D T, 1996. Regional convergence clusters across Europe. European Economic Review, 40(3): 951-958. doi: 10.1016/0014-2921(95)00105-0
[29]	Rey S J, 2010. Spatial empirics for economic growth and convergence. Geographical Analysis, 33(3): 195-214. doi: 10.1111/gean.2001.33.issue-3
[30]	Rey S J, Montouri B D, 1999. US regional income convergence: A spatial econometric perspective. Regional Studies, 33(2): 143-156.
[31]	Rey S J, Murray A T, Anselin L, 2011. Visualizing regional income distribution dynamics. Letters in Spatial and Resource Sciences, 4(1): 81-90. doi: 10.1007/s12076-010-0048-2
[32]	Rey S J, Ye X, Páez A et al., 2010. Comparative Spatial Dynamics of Regional Systems. Berlin Heidelberg: Springer.
[33]	Song J N, 2015. Research on formation mechanism and evaluation of comprehensive transport efficiency [D]. Xi’an: Chang’an University. (in Chinese)
[34]	Su W S, Liu Y Y, Wang S J et al., 2018. Regional inequality, spatial spillover effects, and the factors influencing city-level energy-related carbon emissions in China, Journal of Geographical Sciences, 28(4): 495-513. doi: 10.1007/s11442-018-1486-9
[35]	Sun C Z, Ma Q F, Zhao L S, 2020. Analysis of driving mechanism based on a GWR model of green efficiency of water resources in China. Acta Geographica Sinica, 75(5): 1022-1035. (in Chinese)
[36]	Sun J S, Wang Z H, Li G, 2018. Measuring emission-reduction and energy-conservation efficiency of Chinese cities considering management and technology heterogeneity. Journal of Cleaner Production, 175: 561-571. doi: 10.1016/j.jclepro.2017.12.042
[37]	Tamaki T, Nakamura H, Fujii H et al., 2019. Efficiency and emissions from urban transport: Application to world city-level public transportation. Economic Analysis and Policy, 61: 1-9. doi: 10.1016/j.eap.2019.01.002
[38]	Tan S K, Hu B X, Kuang B et al., 2021. Regional differences and dynamic evolution of urban land green use efficiency within the Yangtze River Delta, China. Land Use Policy, 106, 105449: 1-10.
[39]	Tang T B, You J X, Sun H et al., 2019. Transportation efficiency evaluation considering the environmental impact for China’s freight sector: A parallel data envelopment analysis. Sustainability, 11(18), 5108: 1-24. doi: 10.3390/su11010001
[40]	Tone K, 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3): 498-509. doi: 10.1016/S0377-2217(99)00407-5
[41]	Wang L K, Peng C, Shi W M et al., 2020. Carbon dioxide emissions from port container distribution: Spatial characteristics and driving factors. Transportation Research Part D: Transport and Environment, 82, 102318: 1-11.
[42]	Wang S J, Gao S, Huang Y Y et al., 2020. Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model. Acta Geographica Sinica, 75(6): 1316-1330. (in Chinese)
[43]	Wang S J, Huang Y Y, Zhou Y Q, 2019. Spatial spillover effect and driving forces of carbon emission intensity at the city level in China. Journal of Geographical Sciences, 29(2): 231-252. doi: 10.1007/s11442-019-1594-1
[44]	Wu J, Zhu Q Y, Chu J F et al., 2016. Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transportation Research Part D: Transport and Environment, 48: 1-13. doi: 10.1016/j.trd.2016.07.006
[45]	Xia Z C, Guo Z J, Wang W Y et al., 2021. Joint optimization of ship scheduling and speed reduction: A new strategy considering high transport efficiency and low carbon of ships in port. Ocean Engineering, 233, 109224: 1-13.
[46]	Xu Y, Fan J Q, Xu H C, 2021. Study on the operation efficiency of toll roads in China from the perspective of scale economy. Journal of Advanced Transportation, 8830521: 1-15.
[47]	Yang F, Choi Y, Lee H, 2021. Life-cycle data envelopment analysis to measure efficiency and cost-effectiveness of environmental regulation in China’s transport sector. Ecological Indicators, 126, 107717: 1-8.
[48]	Ye X, Rey S J, 2013. A framework for exploratory space-time analysis of economic data. The Annals of Regional Science, 50(1): 315-339. doi: 10.1007/s00168-011-0470-4
[49]	Yuan C W, Sun L, Li T T et al., 2020. Research on the relationship between environmental regulation and highway transportation efficiency based on PVAR model. Environmental Science & Technology, 43(5): 221-229. (in Chinese)
[50]	Zandalinas S I, Fritschi F B, Mittler R, 2021. Global warming, climate change, and environmental pollution: Recipe for a multifactorial stress combination disaster. Trends in Plant Science, 26(6): 588-599. doi: 10.1016/j.tplants.2021.02.011
[51]	Zhang J X, Cai W Y, Philbin S P et al., 2020. Measuring the capacity utilization of China’s transportation industry under environmental constraints. Transportation Research Part D: Transport and Environment, 85, 102450: 1-21.
[52]	Zhou L, Che L, Zhou C H, 2019. Spatio-temporal evolution and influencing factors of urban green development efficiency in China. Acta Geographica Sinica, 74(10): 2027-2044. (in Chinese)
[53]	Zhou L, Zhou C H, Che L et al., 2020. Spatio-temporal evolution and influencing factors of urban green development efficiency in China. Journal of Geographical Sciences, 30(5): 724-742. doi: 10.1007/s11442-020-1752-5
[54]	Zindani D, Maity S R, Bhowmik S, 2021. Extended TODIM method based on normal wiggly hesitant fuzzy sets for deducing optimal reinforcement condition of agro-waste fibers for green product development. Journal of Cleaner Production, 301, 126947: 1-22.

Type	Spatio-temporal transition form	Symbol
Type 0	Local and neighbouring CTGE as stable.	HH_t→HH_t₊₁, LL_t→LL_t₊₁, HL_t→HL_t₊₁, LH_t→LH_t₊₁
Type 1	Only local CTGE in transition	HH_t→LH_t₊₁, LH_t→HH_t₊₁, HL_t→LL_t₊₁, LL_t→HL_t₊₁
Type 2	Only neighbouring CTGE in transition	HH_t→HL_t₊₁, LH_t→LL_t₊₁, HL_t→HH_t₊₁, LL_t→LH_t₊₁
Type 3	Both local and neighbouring CTGE in transition	HH_t→LL_t₊₁, LH_t→HL_t₊₁, HL_t→LH_t₊₁, LL_t→HH_t₊₁

Indicators	First-level indicators	Second-level indicators	Unit
Inputs	Infrastructure	Total mileage of road, railway, waterway, and pipeline transportation network	10,000 kilometers
	Capital stock	Capital stock in transportation	100 million yuan
	Labor force	Individuals employed in the transportation industry	Individuals
	Energy consumption	Energy consumption in transportation	10,000 tons of standard coal
Outputs	Expected outputs	Traffic added value	100 million yuan
	Expected outputs	Social development index (SDI)	-
	Unexpected output	CO₂ emissions from transportation sector	10,000 tons

t/(t+1)	n	I	II	III	IV
I	28	0.893	0.107	0	0
II	257	0.016	0.922	0.062	0
III	72	0	0.236	0.639	0.125
IV	123	0	0.008	0.065	0.927

Spatial lag	t	n	t+1
Spatial lag	t	n	I	II	III	IV
I	I	0	0	0	0	0
	II	0	0	0	0	0
	III	0	0	0	0	0
	IV	0	0	0	0	0
II	I	28	0.893	0.107	0	0
	II	130	0.031	0.915	0.084	0
	III	35	0	0.228	0.686	0.086
	IV	47	0	0	0.106	0.894
III	I	0	0	0	0	0
	II	125	0	0.936	0.064	0
	III	31	0	0.258	0.581	0.161
	IV	52	0	0.019	0.038	0.943
IV	I	0	0	0	0	0
	II	2	0	0.500	0.500	0
	III	6	0	0.167	0.666	0.167
	IV	24	0	0	0.042	0.958

Year	Moran’s I	z	p	Year	Moran’s I	z	p
2003	0.2335	2.3390	0.008	2011	0.1703	2.3493	0.040
2004	0.2187	2.1630	0.017	2012	0.1804	1.9644	0.028
2005	0.2120	2.1120	0.015	2013	0.1317	1.5070	0.074
2006	0.1258	1.3507	0.088	2014	0.1547	1.6905	0.049
2007	0.1227	1.3727	0.083	2015	0.1311	1.4382	0.073
2008	0.1123	1.2777	0.097	2016	0.1458	1.5770	0.054
2009	0.0974	1.1687	0.111	2017	0.2403	2.3170	0.013
2010	0.1527	1.6420	0.062	2018	0.2456	2.4775	0.007