A Framework for Assessing Green Capacity Utilization Considering CO2 Emissions in China’s High-Tech Manufacturing Industry
Abstract
:1. Introduction
2. Literature Review
2.1. Performance Assessment of the High-Tech Manufacturing Industry
2.2. Capacity Utilization and Its Measurement
2.3. A Summary of the Literature Review
3. Methodology
3.1. Green CU indicator
3.2. Implications of the Green CU Indicator
- ▪
- and hold simultaneously. Then, the assessed DMU can be said to be at under-utilized capacity and the variable inputs are absolutely insufficient.
- ▪
- and do not hold simultaneously. Then, the assessed DMU can be said to be at under-utilized capacity, and the variable inputs are insufficient.
- ▪
- and hold simultaneously. Then, the assessed DMU can be said to be at fully utilized capacity and the variable inputs are in optimum condition.
- ▪
- and hold simultaneously. Then, the assessed DMU can be said to be at fully utilized capacity and the variable inputs are absolutely redundant.
- ▪
- and do not hold simultaneously. Then, the assessed DMU can be said to be at fully utilized capacity and the variable inputs are redundant.
4. Data and Indicators
4.1. Data Set
4.2. Input and Output Variables
- (i)
- Labor: the annual average number of employed personnel in the high-tech manufacturing industry. In this study, labor is used as an input variable.
- (ii)
- Energy consumption: the total consumption of all energy types in China’s high-tech manufacturing industry during a certain period. It is not easy to directly obtain data on energy consumption directly from the high-tech manufacturing industry. However, it cannot be ignored that the classification criteria for different types of statistical yearbooks in China are based on national economic industry classifications. Hence, according to the specific description of each industrial sector in the National Economic Industry Classification Notes 2017, we selected industrial sectors that are very similar to the high-tech manufacturing industry from the China Energy Statistical Yearbook [38] and CEAD. We used the data on energy consumption as the proxy for energy consumption by the high-tech manufacturing industry. The industrial sectors include medical and pharmaceutical products, chemical fibers, ordinary machinery, equipment for a special purpose, electrical equipment and machinery, electronic and telecommunications equipment, instruments, meters, and cultural and office machinery. The guidelines issued by the Intergovernmental Panel on Climate Change (IPCC) regarding the allocation of greenhouse gas (GHG) emissions [43] include 20 types of energy. This study follows this approach. Different types of energy consumption were converted into uniform unit standard coal equivalents (SCEs) [44]. The conversion coefficients used in this study are shown in Table A1 in Appendix A.
- (iii)
- Assets: economic resources that are owned by the enterprise and that can be assessed in monetary terms, including various capitals, claims, and other rights. In this study, assets are used as a (quasi-)fixed input variable that cannot change quickly in the short term. The Consumer Price Index (CPI) is used to deal with inflation to ensure the continuity and comparability of data, which are shown in Table A2 in Appendix A.
- (iv)
- Patent: the number of patent applications filed by China’s high-tech manufacturing industry for a certain period, which is the sum of the number of invention patent applications, utility model patent applications, and design patent applications. As a relevant carrier of knowledge output, patents reflect the contribution of industry development to knowledge growth. Furthermore, the number of patents may be the most applicable proxy for knowledge growth [1,16,22,45]. In this study, the patent is used as a desirable output.
- (v)
- Sales revenue: the sales revenue of new products is a continuous source of capital and development, reflecting the growth potential of the industry’s economic output. In this study, sales revenue is also used as a proxy for desirable output. The CPI is used to deflate the data to ensure the comparability of continuous data.
- (vi)
- CO2 emissions: the average GHG emissions generated by China’s high-tech manufacturing industry during the life cycle of certain products. CO2 emissions are the undesirable output in this study. However, CO2 emission data could not be obtained directly from any of China’s statistical yearbooks. Therefore, these data are estimated based on the different types of energy consumption. IPCC provides a general method for estimating CO2 emissions in the 2006 Guidelines for National Greenhouse Gas Inventories [46]. The technique is widely used by national governments, research institutions, and researchers [47,48].
4.3. Descriptive Statistics of the Variables
5. Empirical Results and Discussion
5.1. Provincial Green CU indicator
5.2. Analysis from a Time Perspective
5.3. Analysis from the Regional Perspective
5.4. Analysis Based on the Variable Inputs’ Scale
5.5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
No. | Energy Types | Conversion Factors from Physical Units to Standard Coal Equivalent | No. | Energy Types | Conversion Factors from Physical Units to Standard Coal Equivalent |
---|---|---|---|---|---|
1 | Raw coal | 0.714 | 11 | Kerosene | 1.471 |
2 | Cleaned coal | 0.900 | 12 | Diesel oil | 1.471 |
3 | Other Washed Coal | 0.286 | 13 | Fuel oil | 1.429 |
4 | Briquettes | 0.714 | 14 | Liquefied petroleum gas | 1.714 |
5 | Coke | 0.971 | 15 | Refinery Gas | 1.571 |
6 | Coke oven gas | 0.614 | 16 | Other Petroleum Products | 1.429 |
7 | Other Gas | 0.714 | 17 | Natural gas | 1.330 |
8 | Other Coking Products | 0.714 | 18 | Heat | 0.034 |
9 | Crude oil | 1.429 | 19 | Electricity | 0.123 |
10 | Gasoline | 1.471 | 20 | Other Energy | 1.000 |
Year | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|---|
CPI value | 100.000 | 105.600 | 108.300 | 111.200 | 113.300 | 114.900 |
Energy Types | NCV (PJ/104t, 108m3,tec.) | EF(Mt CO2/PJ) | OE |
---|---|---|---|
Raw coal | 0.209 | 0.087 | 0.885 |
Cleaned coal | 0.263 | 0.087 | 0.885 |
Other Washed Coal | 0.154 | 0.087 | 0.885 |
Briquettes | 0.178 | 0.087 | 0.885 |
Coke | 0.284 | 0.104 | 0.970 |
Coke oven gas | 1.631 | 0.071 | 0.990 |
Other Gas | 0.843 | 0.071 | 0.990 |
Other Coking Products | 0.284 | 0.091 | 0.970 |
Crude oil | 0.418 | 0.073 | 0.980 |
Gasoline | 0.431 | 0.069 | 0.980 |
Kerosene | 0.431 | 0.072 | 0.980 |
Diesel oil | 0.427 | 0.074 | 0.000 |
Fuel oil | 0.418 | 0.077 | 0.980 |
Liquefied petroleum gas | 0.502 | 0.063 | 0.990 |
Refinery Gas | 0.461 | 0.073 | 0.990 |
Other Petroleum Products | 0.418 | 0.074 | 0.980 |
Natural gas | 3.893 | 0.056 | 0.990 |
Non-fossil heat | 0.010 | 0.000 | 0.000 |
Non-fossil electricity | 0.360 | 0.000 | 0.000 |
Other Energy | 0.293 | 0.000 | 0.000 |
Energy Types | Unit | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|---|---|
Heat | Mt CO2/1010KJ | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 |
Electricity | Mt CO2/108KWh | 0.063 | 0.064 | 0.063 | 0.062 | 0.057 | 0.054 |
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Authors (Year) | China’s Industries | Input Indicators | Output Indicators |
---|---|---|---|
Zhang and Chen [22] | High-technology industries | Stage 1: R&D personnel, R&D expenses Stage 2: Patent applications, number of patents in force | Stage 1: patent applications, number of patents in force Stage 2: prime operating revenue, sales revenue of new products, export delivery value |
Zhang et al. [2] | High-tech industry | Stage 1: intramural expenditure on R&D, R&D personnel Stage 2: Patent applications, number of patents in force | Stage 1: patent applications, number of patents in force Stage 2: sales revenue of new products and value of contract deals in domestic technical markets |
Shi and Li [40] | Manufacturing industry | Capital stock, labor, and energy | Desirable outputs: manufacturing output Undesirable output: CO2 emissions |
Yang et al. [11] | Manufacturing industries | Labor, asset, and energy | Desirable outputs: gross industrial output value Undesirable output: CO2 emissions |
Kang et al. [41] | Manufacturing industry | Labor, asset, and energy consumption | Desirable output: industrial value-added Undesirable output: CO2 emissions |
Emrouznejad and Yang [42] | Light manufacturing industries | Labor, asset and energy | Desirable outputs: Gross Industrial Output Value Undesirable output: CO2 emissions |
Variables | Units | Definitions | Data Resource | |
---|---|---|---|---|
Variable inputs | Labor | Person | The annual average number of employed personnel | China Statistics Yearbook on High Technology Industry |
Energy consumption | Kt SCE | Total consumption of all energy types | China Energy Statistical Yearbook; http://www.ceads.net/data/ | |
Fixed input | Assets | 100 million RMB yuan | Economic resources that can be measured in monetary terms | China Statistics Yearbook on High Technology Industry |
Desirable outputs | Patent | Piece | The number of patent applications | China Statistics Yearbook on High Technology Industry |
Sales revenue | 10,000 RMB yuan | Sales revenue of new products | China Statistics Yearbook on High Technology Industry | |
Undesirable output | CO2 emissions | Mt | Average GHG emissions | http://www.ceads.net/data/ |
Year | Statistics | Labor (person) | Energy Consumption (Kt SCE) | Assets (100 million RMB yuan) | Patent (piece) | Sales Revenue (10,000 RMB yuan) | CO2 Emissions (Mt) |
---|---|---|---|---|---|---|---|
2015 | Mean | 482,950.036 | 4077.859 | 3602.437 | 5653.143 | 12,862,903.146 | 11.741 |
St.Dev. | 814,922.631 | 4498.175 | 5041.029 | 10,165.915 | 23,065,154.275 | 14.191 | |
Max | 3,890,108.000 | 19,091.098 | 23,396.519 | 50,629.000 | 107,300,765.883 | 58.801 | |
Min | 11,270.000 | 66.389 | 208.790 | 64.000 | 109,450.827 | 0.258 | |
Median | 268,610.000 | 2497.846 | 2134.682 | 2400.500 | 5,395,556.571 | 7.184 | |
2014 | Mean | 472,663.357 | 4014.042 | 3154.955 | 5951.643 | 12,210,364.046 | 11.515 |
St.Dev. | 810,213.032 | 4168.320 | 4529.664 | 11,551.278 | 20,957,089.708 | 14.424 | |
Max | 3,872,690.000 | 17,194.896 | 20,926.831 | 58,119.000 | 95,829,398.941 | 60.281 | |
Min | 7417.000 | 64.732 | 100.177 | 62.000 | 110,744.925 | 0.229 | |
Median | 249,207.000 | 2574.036 | 1766.284 | 2112.500 | 4,416,525.596 | 6.335 | |
2013 | Mean | 461,610.571 | 3926.611 | 2794.155 | 5104.643 | 10,028,263.489 | 11.895 |
St.Dev. | 802,099.366 | 4119.565 | 4059.598 | 9757.610 | 18,876,946.751 | 14.821 | |
Max | 3,803,831.000 | 16,216.372 | 18,447.032 | 49,691.000 | 87,848,688.849 | 61.798 | |
Min | 6726.000 | 52.269 | 77.158 | 58.000 | 126,353.417 | 0.229 | |
Median | 224,917.000 | 2474.826 | 1483.138 | 2117.000 | 2,983,875.899 | 5.547 | |
2012 | Mean | 452,714.357 | 3340.708 | 2521.541 | 4564.250 | 8,431,725.465 | 11.370 |
St.Dev. | 811,198.714 | 3701.806 | 3768.429 | 8918.651 | 17,326,078.498 | 14.508 | |
Max | 3,842,156.000 | 14,221.251 | 17,085.780 | 45,449.000 | 78,666,235.457 | 58.542 | |
Min | 7161.000 | 47.387 | 72.484 | 39.000 | 94,606.648 | 0.210 | |
Median | 214,612.000 | 1848.413 | 1271.699 | 1689.000 | 1,959,469.067 | 5.443 | |
2011 | Mean | 409,142.536 | 3383.951 | 2189.259 | 3615.071 | 7,596,671.672 | 11.661 |
St.Dev. | 763,903.207 | 3618.830 | 3453.653 | 7703.026 | 15,231,155.433 | 14.360 | |
Max | 3,614,903.000 | 13,713.957 | 15,730.398 | 39,338.000 | 69,700,292.614 | 55.918 | |
Min | 5612.000 | 42.001 | 56.723 | 54.000 | 61,770.833 | 0.199 | |
Median | 204,627.000 | 2092.588 | 1041.572 | 1333.000 | 2,227,925.663 | 6.044 | |
2010 | Mean | 389,591.429 | 3307.680 | 2046.279 | 2130.964 | 5,843,456.393 | 10.604 |
St.Dev. | 749,772.004 | 3645.856 | 3440.257 | 5089.295 | 12,195,594.898 | 13.348 | |
Max | 3,547,488.000 | 13,288.428 | 16,273.900 | 26,740.000 | 60,464,340.000 | 50.501 | |
Min | 6708.000 | 38.750 | 55.700 | 12.000 | 15,401.000 | 0.164 | |
Median | 185,207.000 | 1961.722 | 900.250 | 691.000 | 1,354,860.500 | 5.815 |
DMUs | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | Average | |
---|---|---|---|---|---|---|---|---|
Value | Rank | |||||||
Beijing | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Tianjin | 0.050 | 0.002 | 0.000 | 0.000 | 0.000 | 0.062 | 0.019 | 19 |
Hebei | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Shanxi | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Inner Mongolia | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Liaoning | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.037 | 0.006 | 15 |
Jilin | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Heilongjiang | 0.000 | 0.000 | 0.000 | 0.000 | 0.243 | 0.049 | 0.049 | 24 |
Shanghai | 0.060 | 0.000 | 0.000 | 0.002 | 0.083 | 0.040 | 0.031 | 22 |
Jiangsu | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Zhejiang | 0.442 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.074 | 26 |
Anhui | 0.425 | 0.145 | 0.017 | 0.056 | 0.186 | 0.039 | 0.145 | 28 |
Fujian | 0.000 | 0.000 | 0.000 | 0.044 | 0.019 | 0.051 | 0.019 | 21 |
Jiangxi | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Shandong | 0.000 | 0.000 | 0.000 | 0.033 | 0.159 | 0.124 | 0.053 | 25 |
Henan | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Hubei | 0.000 | 0.000 | 0.000 | 0.029 | 0.000 | 0.049 | 0.013 | 18 |
Hunan | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Guangdong | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Guangxi | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Hainan | 0.599 | 0.000 | 0.000 | 0.022 | 0.000 | 0.000 | 0.104 | 27 |
Chongqing | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Sichuan | 0.000 | 0.000 | 0.000 | 0.000 | 0.076 | 0.038 | 0.019 | 20 |
Guizhou | 0.000 | 0.005 | 0.005 | 0.000 | 0.000 | 0.055 | 0.011 | 16 |
Yunnan | 0.018 | 0.009 | 0.050 | 0.000 | 0.000 | 0.000 | 0.013 | 17 |
Shaanxi | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Gansu | 0.110 | 0.016 | 0.039 | 0.005 | 0.000 | 0.053 | 0.037 | 23 |
Ningxia | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1 |
Eastern region | 0.115 | 0.000 | 0.000 | 0.010 | 0.026 | 0.028 | 0.030 | - |
Middle region | 0.071 | 0.024 | 0.003 | 0.014 | 0.031 | 0.015 | 0.026 | - |
Western region | 0.014 | 0.003 | 0.010 | 0.001 | 0.008 | 0.016 | 0.009 | - |
Northeastern region | 0.000 | 0.000 | 0.000 | 0.000 | 0.081 | 0.029 | 0.018 | - |
Average | 0.061 | 0.006 | 0.004 | 0.007 | 0.027 | 0.021 | 0.021 | - |
Year | Average | Average | Average CU |
---|---|---|---|
2010 | 0.448 | 0.508 | 0.061 |
2011 | 0.447 | 0.453 | 0.006 |
2012 | 0.459 | 0.463 | 0.004 |
2013 | 0.415 | 0.422 | 0.007 |
2014 | 0.363 | 0.391 | 0.027 |
2015 | 0.345 | 0.366 | 0.021 |
Average | 0.413 | 0.434 | 0.021 |
Year | ||||
---|---|---|---|---|
2010 | 10,908,560.000 | 10,225,762.079 | 92,615.034 | 52,833.226 |
2011 | 11,455,991.000 | 10,386,412.015 | 94,750.627 | 64,537.784 |
2012 | 12,676,002.000 | 11,306,516.038 | 93,539.811 | 65,652.029 |
2013 | 12,925,096.000 | 12,624,306.251 | 109,945.099 | 71,073.112 |
2014 | 13,234,574.000 | 12,883,696.261 | 112,393.171 | 73,368.365 |
2015 | 13,522,601.000 | 14,204,768.163 | 114,180.041 | 77,408.230 |
Year | ||
---|---|---|
2010 | 0.079 * | 0.001 *** |
2011 | 0.003 *** | 0.013 ** |
2012 | 0.002 *** | 0.003 *** |
2013 | 0.334 | 0.016 ** |
2014 | 0.616 | 0.001 *** |
2015 | 0.334 | 0.002 *** |
Anhui | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|---|
189,879.648 | 252,315.153 | 261,694.649 | 296,279.782 | 398,366.905 | 487,423.505 | |
958.370 | 1858.608 | 1328.680 | 1427.159 | 1292.159 | 2098.346 | |
1.297 | 1.684 | 1.397 | 1.444 | 1.580 | 1.826 | |
0.4960 | 0.912 | 0.786 | 0.715 | 0.612 | 0.901 | |
146,412.000 | 149,818.000 | 187,326.000 | 205,182.000 | 252,133.000 | 266,994.000 | |
1932.689 | 2037.722 | 1689.883 | 1994.827 | 2110.712 | 2329.205 |
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Wang, Y.; Pan, J.; Pei, R.; Yang, G.; Yi, B. A Framework for Assessing Green Capacity Utilization Considering CO2 Emissions in China’s High-Tech Manufacturing Industry. Sustainability 2020, 12, 4424. https://doi.org/10.3390/su12114424
Wang Y, Pan J, Pei R, Yang G, Yi B. A Framework for Assessing Green Capacity Utilization Considering CO2 Emissions in China’s High-Tech Manufacturing Industry. Sustainability. 2020; 12(11):4424. https://doi.org/10.3390/su12114424
Chicago/Turabian StyleWang, Ya, Jiaofeng Pan, Ruimin Pei, Guoliang Yang, and Bowen Yi. 2020. "A Framework for Assessing Green Capacity Utilization Considering CO2 Emissions in China’s High-Tech Manufacturing Industry" Sustainability 12, no. 11: 4424. https://doi.org/10.3390/su12114424