Article: Calculation of carbon emission footprint of ecotourism energy driven by big data Journal: International Journal of Environment and Pollution (IJEP) 2026 Vol.76 No.8 pp.62 - 81 Abstract: Aiming at the problems of coarse data granularity, incomplete coverage, and lack of dynamics in the calculation of carbon emissions in ecotourism, a dynamic calculation framework driven by big data is constructed, and an empirical study is conducted using a rural tourism scenic spot as a case. Ecotourism is different from mass tourism. Its carbon emissions show the characteristics of decentralised transportation modes, a clean energy structure, and high dependence on surrounding facilities. This paper integrates heterogeneous multi-source data, including global positioning system (GPS) trajectories, energy consumption at scenic spots, transportation use, and social media comments, to build a unified data model and enrich the data dimension. For data preprocessing, the Kalman filter is used to smooth the GPS trajectory and eliminate jump-point errors. Inderscience Publishers - linking academia, business and industry through research

Title: Calculation of carbon emission footprint of ecotourism energy driven by big data

Authors: Shuang Li

Addresses: School of Tourism and Culinary Arts, Zhejiang Business College, Hangzhou, 310053, Zhejiang, China

Abstract: Aiming at the problems of coarse data granularity, incomplete coverage, and lack of dynamics in the calculation of carbon emissions in ecotourism, a dynamic calculation framework driven by big data is constructed, and an empirical study is conducted using a rural tourism scenic spot as a case. Ecotourism is different from mass tourism. Its carbon emissions show the characteristics of decentralised transportation modes, a clean energy structure, and high dependence on surrounding facilities. This paper integrates heterogeneous multi-source data, including global positioning system (GPS) trajectories, energy consumption at scenic spots, transportation use, and social media comments, to build a unified data model and enrich the data dimension. For data preprocessing, the Kalman filter is used to smooth the GPS trajectory and eliminate jump-point errors.

Keywords: big data driven; ecotourism energy; carbon footprint calculation; multi-regional input-output model; LSTM; long short-term memory; multi-regional input-output.

DOI: 10.1504/IJEP.2026.156121

International Journal of Environment and Pollution, 2026 Vol.76 No.8, pp.62 - 81

Received: 05 Aug 2025
Accepted: 06 Feb 2026
Published online: 04 Sep 2026 *

Title: Calculation of carbon emission footprint of ecotourism energy driven by big data

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