The Impact of Optimizing Industrial Energy Efficiency on Agricultural Development in OECD Countries
Abstract
:1. Introduction
2. Literature Review
3. Research Method
3.1. Dynamic Network SBM, DN-SBM
- (1)
- Inputs and outputs
- (2)
- Links
- (3)
- Carry-overs
Period and Sector Efficiencies
- (1)
- Period efficiency, as is shown in Formula (3):
- (2)
- sector efficiency, as is shown in Formula (4):
- (3)
- Sector period efficiency is defined as follows, as is shown in Formula (5):
3.2. Dynamic Network Malmquist Total Factor Productivity, DN-TFP
Overall DN-TFP
4. Empirical Analysis
4.1. Data Source
4.1.1. Variable Description
4.1.2. Descriptive Statistical Analysis
4.2. DN-SBM Empirical Result
4.2.1. The Industrial Sector Efficiency
4.2.2. Agricultural Sector Efficiency
4.2.3. DN-SBM Overall Efficiency
4.3. DN-TFP Empirical Results
4.4. Policy Implications and Discussion
5. Conclusions
- (1)
- The average efficiency value of the OECD industrial sector during the study period is 0.8719, with a maximum value of 1 and a minimum value of 0.5835. The efficiency values of 14 countries are higher than the average level, and the efficiency values of 17 countries are lower than the average level. The countries with the lowest efficiency values are Portugal (0.7147), France (0.6002), and Sweden (0.5835).
- (2)
- The average efficiency value of the agricultural sector is 0.7666, with a maximum value of 1 and a minimum value of 0.2708 and a standard deviation of 0.2730. The efficiency values of 20 countries are higher than the average level, and the efficiency values of 11 countries are lower than the average level. The countries with the lowest efficiency values are Estonia (0.3134), Slovenia (0.2890), and the Slovak Republic (0.2708).
- (3)
- The overall average value of DN-SBM is 0.7411, with a maximum value of 1 and a minimum value of 0.3607 and a standard deviation of 0.2221. The efficiency values of 17 countries are higher than the average level, and 14 countries are lower than the average level. The countries with the lowest efficiency values are Finland (0.4367), the Slovak Republic (0.3649), and Estonia (0.3607).
- (4)
- The DN-TFP average value is 1.0776, indicating a slight improvement trend. Japan (1.7059) shows the best productivity performance, while Turkey (0.8489) has the lowest DN-TFP value with a standard deviation of 0.1681. DN-TFP values for 22 OECD countries are greater than 1, indicating a trend in progress. DN-TFP values for 9 countries are less than 1, indicating a declining trend. The DN-TFP values for Australia (0.9645), Slovenia (0.9126), and Turkey (0.8489) are the lowest.
- (5)
- This study further compares DN-SBM and DN-TFP and finds that the overall efficiency values for five countries, including Germany (1.2333), Israel (1.0617), Luxembourg (1.0131), Netherlands (1.139), and Switzerland (1.0296), are 1, and productivity has improved. Among the three countries with lower efficiency values, Finland (0.4367, 1.0168) and Slovakia (0.3649, 1.0532) have improved productivity, while Estonia (0.3607, 0.9795), with the lowest efficiency value, not only has lower efficiency but also a decline in productivity. Eight countries have a decline in productivity, but there is still room for improvement in efficiency.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Unit | |
---|---|---|
Industrial Sector Input | Employment in industry | Person |
Gross capital formation | Current Millions of US dollars | |
Electricity | Gigawatt hours | |
Output | Industry value added | Current Millions of US dollars |
Link | Greenhouse Gas | Thousand tons CO2-eq |
Agricultural sector Input | Employment in agriculture | Person |
Land under cereal production | Hectares | |
Precipitation | mm/year | |
Output | Value of Agricultural Production | Current Millions of US dollars |
Carry over | Forest area | sq. km |
Employment in Industry | Gross Capital Formation | Electricity | Industry Value Added | Greenhouse Gas | ||
---|---|---|---|---|---|---|
Max | 2015 | 37,445,319.59 | 3,859,763 | 4,109,219 | 33,839,77.115 | 6,689,006.13 |
2016 | 37,818,786.61 | 3,844,982 | 4,119,445 | 3,373,013.267 | 6,537,871.03 | |
2017 | 39,437,363.87 | 4,558,260 | 4,236,927 | 3,908,533.954 | 6,689,006.13 | |
2018 | 39,437,363.87 | 4,558,260 | 4,236,927 | 3,908,533.954 | 6,689,006.13 | |
2019 | 39,437,363.87 | 4,558,260 | 4,236,927 | 3,908,533.954 | 6,689,006.13 | |
Min | 2015 | 39,921 | 3398.974485 | 2738 | 3537.62447 | 4746.024 |
2016 | 38,716 | 4384.008718 | 2168 | 4064.186221 | 4692.48 | |
2017 | 35,956 | 5376.285015 | 2204.811 | 4846.374721 | 4776.967 | |
2018 | 40,384 | 5850.274779 | 2170.172 | 5161.739529 | 4847.088 | |
2019 | 38,377 | 5243.821439 | 1877.378 | 4918.171152 | 4713.009 | |
Average | 2015 | 4,280,958.438 | 299,316.6691 | 289,633.9 | 296,547.8839 | 434,829.016 |
2016 | 4,322,013.254 | 303,525.6767 | 291,647.3 | 300,008.4507 | 430,037.309 | |
2017 | 4,382,999.656 | 321,048.2218 | 292,560.8 | 315,344.2028 | 429,735.341 | |
2018 | 4,447,133.462 | 345,313.9125 | 297,112.1 | 335,314.2987 | 431,342.955 | |
2019 | 4,451,693.684 | 352,894.9669 | 294,085.8 | 334,365.6366 | 632,357.843 | |
St Dev. | 2015 | 7,464,689.608 | 704,040.3221 | 741,776.6 | 636,356.3478 | 1,197,366.58 |
2016 | 7,528,023.506 | 706,333.5941 | 743,027.9 | 644,511.2976 | 1,170,671.86 | |
2017 | 7,604,771.65 | 742,329.5852 | 737,329.2 | 679,785.36 | 1,163,663.02 | |
2018 | 7,726,565.533 | 796,135.8071 | 763,085.2 | 721,168.3267 | 1,194,822.81 | |
2019 | 7,789,510.195 | 831,098.8412 | 754,459.9 | 734,050.0275 | 1,623,890.78 | |
Employment in agriculture | Land under cereal production | Precipitation | Value of Agricultural Production | Forest area | ||
Max | 2015 | 7,476,523.112 | 58,124,740 | 1940 | 2,790,602.87 | 3,100,950 |
2016 | 7,319,259.506 | 58,445,763 | 1940 | 2,722,427 | 3,100,950 | |
2017 | 7,489,642.452 | 58,445,763 | 1940 | 2,846,178.41 | 3,100,950 | |
2018 | 7,489,642.452 | 58,445,763 | 1940 | 2,846,178.41 | 3,100,950 | |
2019 | 7,489,642.452 | 58,445,763 | 1940 | 2,846,178.41 | 3,100,950 | |
Min | 2015 | 3348 | 1455 | 435 | 2373.82 | 481.6 |
2016 | 3309 | 2300 | 435 | 2297.58 | 486.6 | |
2017 | 4645 | 2100 | 435 | 2763.93 | 493.8 | |
2018 | 3608 | 1500 | 435 | 2838.07 | 500.4 | |
2019 | 2414 | 2200 | 435 | 2637.91 | 506.9 | |
Average | 2015 | 753,733.9634 | 4,489,500.71 | 934.8065 | 253,562.5184 | 217,360.311 |
2016 | 745,114 | 4,424,700.419 | 934.8065 | 251,158.6084 | 217,753.913 | |
2017 | 747,157 | 4,271,956.129 | 934.8065 | 263,145.9048 | 217,794.477 | |
2018 | 731,794.0176 | 4,193,802.097 | 934.8065 | 267,690.7819 | 217,940.652 | |
2019 | 713,782.3127 | 4,176,708.194 | 934.8065 | 262,925.7271 | 218,092.742 | |
St Dev. | 2015 | 1,448,894.294 | 10,797,194.8 | 403.6056 | 509,858.478 | 586,418.715 |
2016 | 1,418,468.485 | 10,787,376.53 | 403.6056 | 499,627.1191 | 587,007.838 | |
2017 | 1,444,949.138 | 9,988,974.662 | 403.6056 | 518,714.6516 | 586,488.961 | |
2018 | 1,402,110.18 | 9,968,133.91 | 403.6056 | 515,326.6791 | 586,468.461 | |
2019 | 1,348,335.55 | 9,790,477.275 | 403.6056 | 515,053.9373 | 586,455.652 |
DMU | Industrial Sector | Agricultural Sector | Overall | DMU | Industrial Sector | Agricultural Sector | Overall |
---|---|---|---|---|---|---|---|
Germany | 1 | 1 | 1 | Chile | 0.7874 | 0.8271 | 0.7038 |
Iceland | 1 | 1 | 1 | Turkey | 0.8353 | 0.8134 | 0.6880 |
Israel | 1 | 1 | 1 | France | 0.6002 | 0.9945 | 0.6505 |
Luxembourg | 1 | 1 | 1 | Portugal | 0.7147 | 0.5909 | 0.5447 |
Netherlands | 1 | 1 | 1 | Austria | 0.7453 | 0.5248 | 0.5257 |
Switzerland | 1 | 1 | 1 | Hungary | 0.7510 | 0.5587 | 0.5166 |
Australia | 1 | 0.9564 | 0.9714 | Poland | 1 | 0.3286 | 0.5020 |
United States | 0.9519 | 1 | 0.9637 | Slovenia | 1 | 0.2890 | 0.4575 |
Ireland | 1 | 0.9126 | 0.9418 | Sweden | 0.5835 | 0.4729 | 0.4520 |
Greece | 0.9921 | 0.9339 | 0.9302 | Czech Republic | 0.8533 | 0.3447 | 0.4515 |
Italy | 1 | 0.9512 | 0.8912 | Finland | 0.7619 | 0.3608 | 0.4367 |
Japan | 0.8504 | 1 | 0.8839 | Slovak Republic | 0.7898 | 0.2708 | 0.3649 |
Denmark | 0.8305 | 1 | 0.8484 | Estonia | 0.7325 | 0.3134 | 0.3607 |
New Zealand | 0.8675 | 1 | 0.8322 | Max | 1 | 1 | 1 |
Spain | 0.8062 | 0.9813 | 0.8081 | Min | 0.5835 | 0.2708 | 0.3607 |
Norway | 1 | 0.6366 | 0.7632 | Average | 0.8719 | 0.7666 | 0.7411 |
Belgium | 0.7802 | 0.8921 | 0.7458 | StDev. | 0.1288 | 0.2730 | 0.2221 |
United Kingdom | 0.7951 | 0.8113 | 0.7406 |
DMU | 2015–2016 | 2016–2017 | 2017–2018 | 2018–2019 | Ave. | DMU | 2015–2016 | 2016–2017 | 2017–2018 | 2018–2019 | Ave. |
---|---|---|---|---|---|---|---|---|---|---|---|
Japan | 1.2858 | 0.9401 | 1.0719 | 6.5367 | 1.7059 | Luxembourg | 1 | 0.9602 | 1.0961 | 1.0008 | 1.0131 |
Spain | 1.2384 | 1.1153 | 1.1853 | 2.8674 | 1.4719 | Italy | 0.9305 | 1.0658 | 1.1274 | 0.937 | 1.0117 |
Ireland | 0.8485 | 1.229 | 1.1257 | 2.9084 | 1.3593 | France | 0.8208 | 1.212 | 1.2907 | 0.8153 | 1.0115 |
Germany | 0.9443 | 1.0192 | 0.8734 | 2.752 | 1.2333 | Czech Republic | 1.0138 | 1.0338 | 1.0032 | 0.9948 | 1.0113 |
Belgium | 0.8983 | 1.3411 | 1.3443 | 1.0669 | 1.1465 | Austria | 0.9984 | 1.0388 | 1.0608 | 0.932 | 1.0063 |
Netherlands | 1.267 | 1.1844 | 0.9733 | 1.1523 | 1.139 | Norway | 0.7691 | 1.3899 | 1.3443 | 0.6851 | 0.9961 |
United Kingdom | 0.8842 | 1.0322 | 1.0795 | 1.6755 | 1.1335 | United States | 0.9333 | 1.081 | 0.9901 | 0.9627 | 0.9903 |
Chile | 1.2176 | 1.1579 | 1.1958 | 0.9454 | 1.1236 | Sweden | 0.9759 | 1.0896 | 0.9927 | 0.8996 | 0.9872 |
Poland | 1.4331 | 0.7494 | 0.666 | 1.8547 | 1.0732 | Estonia | 0.8287 | 1.186 | 0.9405 | 0.9958 | 0.9795 |
New Zealand | 1.0633 | 1.2644 | 1.146 | 0.8495 | 1.0696 | Hungary | 1.0556 | 0.9924 | 0.9155 | 0.9402 | 0.9745 |
Israel | 1.1053 | 1.1453 | 0.9629 | 1.0423 | 1.0617 | Iceland | 1.042 | 1.0124 | 0.9801 | 0.8697 | 0.9738 |
Slovak Republic | 1.0776 | 0.9875 | 1.1845 | 0.9762 | 1.0532 | Australia | 0.9667 | 1.1301 | 0.9775 | 0.8102 | 0.9645 |
Denmark | 1.1021 | 1.1714 | 0.8275 | 1.1281 | 1.0477 | Slovenia | 1.7484 | 0.6294 | 0.6305 | 0.9994 | 0.9126 |
Portugal | 0.95 | 1.0522 | 1.0054 | 1.1419 | 1.035 | Turkey | 0.8543 | 0.8135 | 0.6649 | 1.1237 | 0.8489 |
Switzerland | 0.9886 | 1.0177 | 1.1197 | 0.9975 | 1.0296 | Max | 1.7484 | 1.3899 | 1.3443 | 6.5367 | 1.7059 |
Greece | 0.9762 | 1.0601 | 0.9992 | 1.0678 | 1.0251 | Min | 0.7691 | 0.6294 | 0.6305 | 0.6851 | 0.8489 |
Finland | 0.9785 | 1.0715 | 1.014 | 1.0056 | 1.0168 | Average | 1.0386 | 1.0701 | 1.0254 | 1.3850 | 1.0776 |
StDev. | 0.1996 | 0.1563 | 0.1752 | 1.1220 | 0.1681 |
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Shang, H.; Feng, Y.; Lu, C.-C.; Yang, C.-Y. The Impact of Optimizing Industrial Energy Efficiency on Agricultural Development in OECD Countries. Sustainability 2023, 15, 6084. https://doi.org/10.3390/su15076084
Shang H, Feng Y, Lu C-C, Yang C-Y. The Impact of Optimizing Industrial Energy Efficiency on Agricultural Development in OECD Countries. Sustainability. 2023; 15(7):6084. https://doi.org/10.3390/su15076084
Chicago/Turabian StyleShang, Haiyang, Ying Feng, Ching-Cheng Lu, and Chih-Yu Yang. 2023. "The Impact of Optimizing Industrial Energy Efficiency on Agricultural Development in OECD Countries" Sustainability 15, no. 7: 6084. https://doi.org/10.3390/su15076084
APA StyleShang, H., Feng, Y., Lu, C. -C., & Yang, C. -Y. (2023). The Impact of Optimizing Industrial Energy Efficiency on Agricultural Development in OECD Countries. Sustainability, 15(7), 6084. https://doi.org/10.3390/su15076084