Participation in global value chain and green technology progress: evidence from big data of Chinese enterprises

Environmental Science and Pollution Research

Volumn 24, Issue 2, 2017, Pages 1648-1661

  Song, Malin ^a   Wang, Shuhong ^b  

^a ANHUI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^b OCEAN UNIVERSITY OF CHINA   (China)

Author keywords

Big data; Global value chain; Green technology; Productivity and competitiveness

Indexed keywords

COMPETITIVENESS; ENVIRONMENTAL TECHNOLOGY; ENVIRONMENTAL VALUES; GLOBAL CHANGE; INVESTMENT; PANEL DATA; PARTICIPATORY APPROACH; PRODUCTIVITY; RESEARCH AND DEVELOPMENT; STATE OWNED ENTERPRISE;

CHINA;

CHINA; ECONOMICS; ENVIRONMENTAL PROTECTION; HUMAN; INFORMATION SCIENCE; INTERNATIONAL COOPERATION; ORGANIZATION; ORGANIZATION AND MANAGEMENT; STATISTICS AND NUMERICAL DATA;

CHINA; CONSERVATION OF NATURAL RESOURCES; HUMANS; INFORMATICS; INTERNATIONALITY; ORGANIZATIONS; OWNERSHIP;

EID: 84992735157     PISSN: 09441344     EISSN: 16147499     Source Type: Journal    
DOI: 10.1007/s11356-016-7925-1     Document Type: Article

References (58)

1. Acemoglu D (1998) Why do new technologies complement skills? Directed technical change and wage inequality. Q J Econ 113:1055–1089
2. Acemoglu D (2002) Directed technical change. Review of Economic Studies 69:781–781
3. Acemoglu D (2003) Labor and capital-augmenting technical change. J Eur Econ Assoc 1:1–37
4. Acemoglu D (2007) Equilibrium bias of technology. Econometrica 75:1371–1410
5. Acemoglu D, Aghion P, Bursztyn L, Hemous D (2012) The environment and directed technical change. Am Econ Rev 102:131–166
6. Acemoglu D, Gancia G, Zilibotti F (2015) Offshoring and directed technical change. American Economic Journal–Macroeconomics 7:84–122
7. Aghion P, Howitt P (1992) A model of growth through creative destruction. Econometrica 60:323–351
8. Aghion P, Dechezleprêtre A, Hemous D, Martin R, Van Reenen J (2012) Carbon taxes, path dependency and directed technical change: evidence from the auto industry. NBER Working Paper 18596
9. Antràs P, Chor D (2013) Organizing the global value chain. Econometrica 81:2127–2204
10. Antràs P, Helpman E (2004) Global sourcing. J Polit Econ 112:552–580
11. Aronova E, Baker KS, Oreskes N (2010) Big science and big data in biology: from the international geophysical year through the international biological program to the long term ecological research (LTER) network, 1957–present. Hist Stud Nat Sci 40:183–224
12. Baldwin J, Yan B (2014) Global value chains and the productivity of Canadian manufacturing firms. Mimeo, Statistics Canada, Analytical Studies Branch
13. Bernard AB, Eaton J, Jensen JB, Kortum S (2003) Plants and productivity in international trade. Am Econ Rev 93:1268–1290
14. Cao BR, Wang SH (2016) Opening up, international trade, and green technology progress in China. J Clean Prod. doi:10.1016/j.jclepro. 2016.08.145
15. Chambers RG, Färe R, Grosskopf S (1996) Productivity growth in APEC countries. Pac Econ Rev 1:181–190
16. Chen P, Yu M (2014) Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries. Ann Oper Res 214:143–165
17. Chiara P, Ivete SGF (2011) Competition and innovation in Luxembourg. Journal of Industry Competition and Trade 12:93–117
18. Chor D, Manova K, Yu Z, (2014) The global production line position of Chinese firms. Mimeo, Standard University
19. Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. Journal of Environment Management 51:229–240
20. Costinot A, Vogel J, Wang S (2013) An elementary theory of global supply chains. Rev Econ Stud 80:109–144
21. do Paço AMF, Raposo MLB, Leal Filho W (2009) Identifying the green consumer: a segmentation study. J Target Meas Anal Mark 17:17–25
22. Ellison AM (2010) Repeatability and transparency in ecological research. Ecology 91:2536–2539
23. Escaith H, Inomata S (2011) Trade patterns and global value chains in East Asia: from trade in goods to trade in tasks. IDE JETRO and World Trade Organization, Geneva
24. Feichtinger G, Lambertini L, Leitmann G, Wrzaczek S (2016) R&D for green technologies in a dynamic oligopoly: Schumpeter, arrow and inverted-U’s. Eur J Oper Res 249:1131–1138
25. Fizel JL, Nunnikhoven TS (1992) Technical efficiency of for-profit and non-profit nursing homes. Manag Decis Econ 13:429–439
26. Fizel JL, Nunnikhoven TS (1993) The efficiency of nursing home chains. Appl Econ 25:49–55
27. Glucker AN, Driessen PP, Kolhoff A, Runhaar HA (2013) Public participation in environmental impact assessment: why, who and how? Environ Impact Assess Rev 43:104–111
28. Grossman GM, Rossi-Hansberg E (2008) Trading tasks: a simple theory of offshoring. Am Econ Rev 98:1978–1997
29. Guvenen F, Kuruscu B (2006) Ben-Porath meets skill-biased technical change: a theoretical analysis of rising inequality. Discussion Paper No. 144
30. Haas DA, Murphy FH (2003) Compensating for non-homogeneity in decision-making units in data envelopment analysis. Eur J Oper Res 144:530–544
31. Humphrey J, Schmitz H (2000) Governance and upgrading: linking industrial cluster and global value chain research. IDS Working Paper, No. 120, Institute of Development Studies, University of Sussex, Brighton
32. Ji YB, Wang YP (2014) Some comments on Antonelli and Quatraro’s paper of measuring effect of biased technology on TFP. J Technol Transf 39:276–280
33. Kennedy C, Thirlwall AP (1972) Surveys in applied economics: technical progress. Econ J 82:11–72
34. Klier T, Linn J (2016) The effect of vehicle fuel economy standards on technology adoption. J Public Econ 133:41–63
35. Kou MT, Chen HH, Wang SY, Shao YM (2016) Measuring efficiencies of multi-period and multi-division systems associated with DEA: an application to OECD countries’ national innovation systems. Expert Syst Appl 46:494–510
36. Lovell K (2003) The decomposition of Malmquist productivity indexes. J Prod Anal 20:437–458
37. Macchiavello R, Morjaria A (2015) The value of relationships: evidence from a supply shock to Kenyan rose exports. Am Econ Rev 105:2911–2945
38. Managi S, Karemera D (2004) Input and output biased technological change in US agriculture. Applied Economic Letter 11:283–286
39. Mannea A, Richels R (2004) The impact of learning-by-doing on the timing and costs of CO2 abatement. Energy Econ 26:603–619
40. Melitz MJ (2003) The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica 71:1695–1725
41. Oh D-H (2010) A global Malmquist of efficiency in data envelopment analysis. European Journal of Operation Research 130:498–509
42. Özdemir V, Badr KF, Dove ES, Endrenyi L, Geraci CJ, Hotez PJ, Milius D, Neves-Pereira M, Pang T, Rotimi CN, Sabra R, Sarkissian CN, Srivastava S, Tims H, Zgheib NK, Kickbusch I (2013) Crowd-funded micro-grants for genomics and “big data”: an actionable idea connecting small (artisan) science, infrastructure science, and citizen philanthropy. Omics: A Journal of Integrative Biology 17:161–172
43. Pijanowski BC, Tayyebi A, Doucette J, Pekin BK, Braun D, Plourde J (2014) A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environ Model Softw 51:250–268
44. Popp D (2004) ENTICE: endogenous technological change in the DICE model of global warming. J Environ Econ Manag 48:742–768
45. Russom P (2011) Big data analytics. TDWI Best Practices Report, Fourth Quarter
46. Saen RF (2007) A new mathematical approach for suppliers selection: accounting for non-homogeneous is important. Appl Math Comput 185:84–95
47. Sarkis J, Cordeiro JJ (2012) Ecological modernization in the electrical utility industry: an application of a bads-goods DEA model of ecological and technical efficiency. Eur J Oper Res 219:386–395
48. Schnase JL, Duffy DQ, Tamkin GS, Nadeau D, Thompson JH, Grieg CM, McInerney MA, Webster WP (2014) MERRA analytic services: meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service. Comput Environ Urban Syst. doi:10.1016/j.compenvurbsys Accessed 2013.12.003.
49. Sexton TR, Sleeper S, Taggart RE (1994) Improving pupil transportation in North Carolina. Interfaces 24:87–103
50. Song ML, Wang SH (2016) Can employment structure promotes environment-biased technical progress? Technological Forecasting & Social Change. doi:10.1016/j.techfore. Accessed 2016.02.16
51. Song ML, Wang SH, Wu KY (2016) Environment-biased technological progress and industrial land use efficiency in China’s new normal. Annuals of Operations Research. doi:10.1007/s10479-016-2307-0
52. Steed CA, Ricciuto DM, Shipman G, Smith B, Thornton PE, Wang D, Shi X, Williams DN (2013) Big data visual analytics for exploratory earth system simulation analysis. Comput Geosci 61:71–82
53. Tien JM (2013) Big data: unleashing information. J Syst Sci Syst Eng 22:127–151
54. Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130:498–509
55. Upward R, Wang Z, Zheng J (2013) Weighing China’s export basket: the domestic content and technology intensity of Chinese exports. J Comp Econ 41:527–543
56. Walden JB, Kirkley JE, Fare R (2012) Productivity change under and individual transferable quota management system. Am J Agric Econ 94:913–928
57. Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. Knowledge and data engineering. IEEE Trans Knowl Data Eng 26:97–107
58. Zografidou E, Prtridis K, Arabatzis G, Dey PK (2016) Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis. Comput Oper Res 66:313–326