<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid development of the world economy, some developing countries only pay attention to the growth rate of GDP, but ignore the importance of sustainable development. The extensive economy, which relies on a lot of energy to drive economic growth, can no longer meet the development requirements of a low-carbon economy. Fundamentally changing this mode of economic growth, optimising the energy structure will become an effective key path. This paper starts with the current situation of the government's development of a low-carbon economy, and analyses the factors affecting it. In order to develop a low-carbon economy, reducing haze pollution and greenhouse gas emissions, energy conservation and emission reduction work of the logistics industry becomes imminent. To analyse the current situation of logistics energy efficiency and structure, qualitative and quantitative research methods are used to measure the impact of these indicators on a low-carbon economy, predict the total energy demand and supply under different scenarios, and formulate the best scenario to optimise the energy structure. Finally, the government put forward the grey prediction management scheme for the development of a low-carbon economy.</p>
</abstract>

With the rapid development of the world economy, some developing countries only pay attention to the growth rate of GDP, but ignore the importance of sustainable development. The extensive economy, which relies on a lot of energy to drive economic growth, can no longer meet the development requirements of a low-carbon economy. Fundamentally changing this mode of economic growth, optimising the energy structure will become an effective key path. This paper starts with the current situation of the government's development of a low-carbon economy, and analyses the factors affecting it. In order to develop a low-carbon economy, reducing haze pollution and greenhouse gas emissions, energy conservation and emission reduction work of the logistics industry becomes imminent. To analyse the current situation of logistics energy efficiency and structure, qualitative and quantitative research methods are used to measure the impact of these indicators on a low-carbon economy, predict the total energy demand and supply under different scenarios, and formulate the best scenario to optimise the energy structure. Finally, the government put forward the grey prediction management scheme for the development of a low-carbon economy.

Translog function in government development of low-carbon economy

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Translog function in government development of low-carbon economy"}

With the rapid development of the world economy, some developing countries only pay attention to the growth rate of GDP, but ignore the importance of...

Years	Observations (10,000 tons)	Predictive values (10,000 tons)	Absolute errors

2009	6066.61	6066.61	0
2010	6689.92	7086.67	−496.76
2011	7624.47	7282.94	241.62
2012	7222.49	7486.7	−262.21
2013	7619.28	7694.1	−74.82
2014	8226.41	7908.21	417.1
2015	8866.67	8128.48	728.19
2016	8268.42	8264.78	12.66
2017	8416.26	8687.28	−171.12
2018	8402.46	8826.46	−422.99

Variable	Regression coefficients	Standard error of regression coefficients	Standardized regression coefficient	T Statistics	VIF value

ɛ	7.1422	2.2547	0	1.8212
γ₀	0.154	0.0907	0.1477	1.7981	0.9458
γ₁	0.0297	0.0417	0.0287	0.07127	0.2795
γ₂	−0.0454	0.1125	−0.0242	−0.2999	0.2755
γ₃	0.0778	0.0559	0.0487	1.1929	0.1787
γ₄	0.0271	0.0197	0.0877	1.8928	0.0159
γ₅	−0.0072	0.0272	−0.0175	−0.1792	0.2821
γ₆	0.0044	0.0022	0.0522	1.9572	0.0202
γ₇	0.0045	0.0017	0.1022	2.7272	0.2989
γ₈	0.014	0.0285	0.0227	0.2728	0.2012
γ₉	0.0049	0.0027	0.0725	1.2102	0.2472
γ₁₀	0.007	0.0024	0.109	2.8871	0.5895
γ₁₁	0.0024	0.0022	0.041	1.0977	0.047
γ₁₂	−0.002	0.0089	−0.015	−0.2275	0.2097
γ₁₃	0.0075	0.0052	0.0728	1.4277	0.0482
γ₁₄	0.0145	0.0074	0.1802	2.2487	0.2471

Translog function in government development of low-carbon economy

Publié en ligne: 30 déc. 2021

Pages: 223 - 238

Reçu: 17 juin 2021

Accepté: 24 sept. 2021

DOI: https://doi.org/10.2478/amns.2021.2.00138

Mots clés
translog function, low carbon economy, grey prediction

© 2021 Siyu Meng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

2009–2018 Total Energy Demand Forecast Training

Ridge regression estimation results

Translog function in government development of low-carbon economy

Publié en ligne: 30 déc. 2021

Pages: 223 - 238

Reçu: 17 juin 2021

Accepté: 24 sept. 2021

DOI: https://doi.org/10.2478/amns.2021.2.00138

Mots cléstranslog function, low carbon economy, grey prediction

© 2021 Siyu Meng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

2009–2018 Total Energy Demand Forecast Training

Ridge regression estimation results

Mots clés
translog function, low carbon economy, grey prediction