| Title |
Strategies for Environmental Protection and Optimization of Ecological Business Economic Growth from the Perspective of Sustainable Development |
| ID_Doc |
78157 |
| Authors |
Ma, L; Liu, XF |
| Title |
Strategies for Environmental Protection and Optimization of Ecological Business Economic Growth from the Perspective of Sustainable Development |
| Year |
2023 |
| Published |
Sustainability, 15, 3 |
| DOI |
10.3390/su15032758 |
| Abstract |
The concept of ecological commercial economy refers to the use of ecological economics principles and system engineering methods to change production and consumption patterns within the scope of the carrying capacity of the ecosystem in order to tap into all of the available resource potential. It develops some economically developed and ecologically efficient industries and builds a culture with reasonable systems, a harmonious society, and a healthy ecological environment. This paper aims to use deep learning algorithms to study environmental protection and the optimization of ecological business economic growth from the perspective of sustainable development. In this regard, this paper proposes a theoretical model of environmental regulation, which aids in the study of the sustainable development of the ecological economy. Through experimental analysis, this study determined that the non-renewable resources of the two cities designated M and N dropped from 82% and 99% in 2017 to 78% and 79% in 2021, a decrease of 3% and 20%, respectively. This shows that the non-renewable resources of the four cities in area A generally showed a downward trend. The experimental results show that the deep learning theory and the environmental regulation model play a specific and effective role in the researching of the ecological business economy. |
| Author Keywords |
ecological business economy; deep learning; environmental regulation model; environmental protection; sustainable development |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000930899200001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2071-1050/15/3/2758/pdf?version=1675826678
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