2025 2015 1 2015 18. 08% 17. 42% 2 3 2015 56. 14% 2008 2010 73. 27% 1 2008 2010 100836 shidan01@ 163. com 210023 ruc - zhangcheng@ 163. com 71742001 71703065 17YJC790195 1 158
2017 10 2014 Zhu et al. 2014 2015 Ngai & Pissarides 2007 2011 Benhima 2013 2015 1 2 3 2013 2008 1. 2005 2007 2015 2015 2016 1 2 3 4 5 159
6 Montobbio 2002 Krüger 2010 2. Dollar & Wei 2007 2017 1 2 3 2011 2015 2015 1. 1 1 EPI 160
2017 10 1 + θ i t IM i t0 Y i t Min TP t = γ EP EP t + γ CP CP t 1 s. t. Y i t EP i t Y i t CP i t Y i t LP i t - m+n 1 + γ j = 1 i = E i t = C i t = L i t t α ij t0 Y j t m Y i i t RT i m i m i 1 - λ i t m L i i t m i t m i - 1 + Ψ i t XF i t0 1 + Y i t RT i t E i t m E i i t C i t m C i i t L i t 1 + λ i t m L i i t 2 3 4 i t EX i t0 5 6 7 8 9 EP t = m i E i t / m i Y i t 10 / m i CP t = m C i i t Y i t 11 1 11 i j t b 1 i = 1 2 m j = 1 2 m + n b = 1 2 k t 0 t TP EP CP LP γ EP γ CP EP CP Y E C L XF IM EX RT 2 θ α ij γ Ψ λ 1 2 11 2 4 5 3 6 7 9 10 11 2. stochastic frontier analysis SFA 1 2 i j 3 161
Battese & Coelli 1995 SFA K L M T 1 LogY it = β 0 + β 1 LogK it + β 2 LogL it + β 3 LogM it + β 4 LogK 2 it + β 5 LogL 2 it + β 6 LogM 2 it + β 7 LogK it LogL it + β 8 LogK it LogM it + β 9 LogL it LogM it + β 10 T t + β 11 T 2 t + β 12 T t LogK it + β 13 T t LogL it + β 14 T t LogM it + V it - U it 12 β U iid N 0 σ 2 u V iid N 0 σ 2 v 12 3. 2015 2015 Kirkley et al. 2002 Cooper et al. 2004 non-discretionary variable model NDSC s - k it PUR 13 PUR it = K it - s - k it /K it 13 30 2003 2015 2000 2002 2011 1 γ OLS 2006 162
2017 10 29 1 1 Y 2 L 3 K K t = K t - 1 1 - δ t + I t /P t 2015 2 I t P t δ t 3 2000 4 M 2001 2007 2008 2015 2003 2007 5 T 1 13 6 E 7 C IPCC 2006 8 EP CP LP 9 IM EX α ij XF 2012 10 γ EP 0. 5 11 TP 4 12 λ 13 θ φ γ ψ 2015 2012 1. WTO 1 2003 0. 6106 K 0 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 2 2015 3 - / 20% 5. 87% 4 163
/ 2015 0. 2348 / 7. 83% 1 1. 6023 / 0. 6784 / 6. 57% 2 2003 3 2015 6 2003 10 2015 16 3 2003 2. 3737 / 0. 0367 / 64. 68 1. 3217 2015 1. 3951 0. 0103 / 135. 45 1. 7296 2. 2015 2 1 2015 898564. 05 938833. 01 4. 48% 2015 211017. 20 180618. 72 14. 41% 609611. 39 526001. 46 13. 72% 0. 4566 / 0. 3763 / 17. 59% 0. 2348 / 0. 1924 / 18. 08% 0. 6784 / 0. 5603 / 17. 42% 1 2 2015 2015 2010 2 1 2 164 1-12 槡 EP 2003 - EP2015 0. 5 0. 75 0. 25 0. 25 0. 75 1% 2% 1%
2017 10 6 1 2015 2015 2010 66. 57% 2 0% 66. 57% 9 2010 2015 0% 0% 66. 57% 17 2015 2010 2 0% 0% 66. 57% 2015 2 0% 66. 57% 2015 1 9 2015 2010 % % R 1 > 0 R 1 0 R 2 > 66. 57% 0 < R 2 66. 57% R 2 0 2015 2010 + 4. 0 2025 3279. 92 5917. 63 80. 42% 4. 0 2025 17 165
3. 1 12 2 10% γ 0. 9693 1% 2 LnK 0. 3410 3. 4377 LnK LnK 0. 0214 1. 9036 T LnL 0. 0078 3. 9581 LnL - 0. 5358-6. 8444 LnL LnL 0. 0745 3. 3137-0. 4902-1. 6558 LnM 1. 4228 11. 1265 LnM LnM - 0. 0624-4. 6877 σ 2 0. 0798 LnK LnL - 0. 1184-4. 7794 T 0. 0433 3. 9220 γ 0. 9693 LnL LnM 0. 0823 2. 3641 T T - 0. 0016-5. 6507 Log-likelihood 630. 0665 Z 1% 5% 10% Ln e 2 2015 2 2015 147085. 61 485067. 62 166
2017 10 26. 02% 0. 74% 2015 3 2015 2010 % % R 3 > 0 R 3 0 R 4 > 18. 03% 0 < R 4 18. 03% R 4 0 3 3 3 6 11 1 4 8 2 3 2 2015 0% 2010 0% 66. 57% 3 2015 0% 2010 0% 18. 03% 6 2015 4 167
4. 2015 8634 6. 19 1. 66 4800 90 0. 9 2014 6 80% Cooper et al. 2004 2015 30 31 DMU 1 4 2015 56. 14% 2011 2015 4 2015 72. 04% 79% 82% 2014 7 10 85. 84% 55. 12% 168
2017 10 TWR TWR TWR TWR TWR 2 4 2008 2010 73. 27% 2015 72. 04% Coelli et al. 2002 2015 Coelli et al. 2002 2015 5. 1978 169
9 2025 2015 1 18. 08% 17. 42% 2 26. 02% 3 2015 2025 170
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Toward the Systemic Optimization of China s Manufacturing Industry Structure Based on Output Structure Optimization and Element Structure Matching Perspectives SHI Dan a and ZHANG Cheng b a a Chinese Academy of Social Sciences b Nanjing University of Finance and Economics Summary The optimized adjustment of China s manufacturing industry structure composed by output structure optimization and element structure optimization is not only one of the core elements of Made in China 2025 but also an important way to promote supply-side reform. How to further understand the upgrading of the industrial structure and the lack of rationalization in the manufacturing industry and the role they play in promoting economic development quality efficiency and upgrading of the main battlefield have become major issues in academia. In the optimization of output structure the current literature introduces energy conservation efficient employment industry coordination and other factors in optimization analysis but it often fails to take full advantage of relevant information on open economy and the contribution of technology. At the same time the current literature discusses the issues of output structure optimization and optimal allocation of production factors yet the two skins phenomenon prevents these issues from being organically combined together. Based on the current literature this paper first uses non-linear programming technology from the perspective of energysaving emission reduction accounting for efficient employment industry balance import and export potential technical level contribution and other factors optimize the output structure of China s double-digit industries in 2015. Then we use the transcendental logarithmic production function model to extract the non-linear relationship between factor inputs and economic outputs and identify the relatively appropriate element structure of the optimized output structure. Finally based on Data Envelopment Analysis technology capital stock is used to estimate and analyze the capacity utilization level before and after optimization. The findings are as follows. First the manufacturing industry structure has huge optimized adjustment potential and can reduce energy intensity and carbon dioxide intensity by 18. 08% and 17. 42% respectively compared with the 2015 values. Second to decrease resource misallocation the input factors of the manufacturing industry require linkage matching especially the capital stock which should be adjusted by a large amplitude after the output structure optimization. Third the results of the calculation of the utilization level of capital stock further show that the utilization rate of manufacturing capacity 56. 14% in 2015 is much lower than the average level 73. 27% in the second half of the 12th Five-year Plan for the national economy 2008-2010 which is affected by the investment inertia and slowdown of economic growth while the capacity utilization after the linkage matching of input factors can be improved to match the latter. The main contributions of this paper are the following. First in the optimization of the manufacturing output structure we focus on the import and export potential indicators from the demand side and the technical level contribution index from the supply side. Second we overcome the two skins phenomenon of the analyses in the current literature by organically combining output structure optimization and factor input matching. Third in the study of element structure matching this article follows the idea of inheritance and criticism thus it relies not only on the extraction of historical information to carry out the initial matching of the element structure but also analyzes the allocation of capital stock focusing on the capital stock overcapacity problem. Keywords Industry Structure Element Structure Overcapacity Energy-saving and Emission-reduction JEL Classification O21 Q01 Q56 172