Sustainable Digital Marketing Model of Geoenergy Resources under Carbon Neutrality Target

Yingge Zhang (), Zhihu Xia, Yanni Li, Anmai Dai and Jie Wang
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Yingge Zhang: School of Public Administration, Xiangtan University, Xiangtan 411100, China
Zhihu Xia: School of Business, Xiangtan University, Xiangtan 411100, China
Yanni Li: School of Business, Xiangtan University, Xiangtan 411100, China
Anmai Dai: School of Business, Xiangtan University, Xiangtan 411100, China
Jie Wang: School of Electrical and Automation, Shandong University of Science and Technology, Qingdao 266400, China

Sustainability, 2023, vol. 15, issue 3, 1-18

Abstract: Geoenergy resources are a new type of clean energy. Carbon neutralization and carbon peaking require significant system reform in the field of energy supply. As a clean, low-carbon, stable and continuous non carbon-based energy, geothermal energy can provide an important guarantee for achieving this goal. Different from the direct way of obtaining energy, ground energy indirectly obtains heat energy from shallow soil and surface water. The vigorous development of geoenergy resources under China’s carbon neutrality goal plays an important role in energy conservation and emission reduction. However, the current carbon trading market is not understood by the public. Therefore, this paper aims to analyze the impact of geoenergy resources on promoting sustainable digital marketing models. Every country around the world is working hard to meet its carbon neutrality goal. This paper analyzed the economic goal of carbon neutrality by analyzing the principle of the carbon trading market. For this reason, this paper designed a carbon trading price prediction algorithm based on the BP neural network (BPNN), which can predict prices in the carbon trading market in order to promote the accurate push of the digital marketing model and finally drive ground energy resources to promote a sustainable digital marketing model. The experimental results of this paper have proved that the price error rate of the BPNN carbon trading price prediction algorithm designed in this paper was within 10%, which was about 20% smaller than the traditional multiple regression analysis algorithm. This proved that the algorithm in this paper has a good performance and can provide accurate information to allow the digital marketing model to achieve sustainable digital marketing.

Keywords: carbon neutrality target; geoenergy resources; digital marketing model; sustainable development; price prediction algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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