Unpacking the green potential of digital trade: evidence from household energy patterns
The rise of digital trade offers new avenues to overcome traditional constraints in energy access and cognition, fostering the adoption of clean energy. Yet, its micro-level mechanisms remain insufficiently explored. Drawing on data from the 2014–2022 China Family Panel Studies (CFPS), this study ma...
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/ade1ab |
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| author | Shi Liang Hongliang Li |
| author_facet | Shi Liang Hongliang Li |
| author_sort | Shi Liang |
| collection | DOAJ |
| description | The rise of digital trade offers new avenues to overcome traditional constraints in energy access and cognition, fostering the adoption of clean energy. Yet, its micro-level mechanisms remain insufficiently explored. Drawing on data from the 2014–2022 China Family Panel Studies (CFPS), this study matches household-level data with provincial digital trade development metrics and employs a Logit fixed-effects model to assess the impact of digital trade on clean energy usage. Results reveal that digital trade significantly increases the likelihood of households adopting clean energy, primarily through enhanced accessibility, improved cognition, and technological diffusion. These findings remain robust across a suite of tests, including hierarchical linear model, fixed-effect adjustments, province-year interaction terms, random-effects models, and double machine learning. Heterogeneity analysis further shows the effect is more pronounced among households with higher social status, lower regional economic development, and those in rural areas. This study empirically uncovers the behavioral transmission channels through which digital trade advances green transitions, offering theoretical insight and policy guidance for integrating digital economies with clean energy agendas. |
| format | Article |
| id | doaj-art-bfc7e952b7584bafa0b56e65e00a73dd |
| institution | DOAJ |
| issn | 2515-7620 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-bfc7e952b7584bafa0b56e65e00a73dd2025-08-20T02:40:21ZengIOP PublishingEnvironmental Research Communications2515-76202025-01-017606501810.1088/2515-7620/ade1abUnpacking the green potential of digital trade: evidence from household energy patternsShi Liang0https://orcid.org/0009-0003-7922-2342Hongliang Li1https://orcid.org/0009-0008-8272-7049College of Public Administration, Huazhong University of Science and Technology , Wuhan, People’s Republic of ChinaInnovation and Entrepreneurship Guidance Center, Hebei University , Baoding, People’s Republic of ChinaThe rise of digital trade offers new avenues to overcome traditional constraints in energy access and cognition, fostering the adoption of clean energy. Yet, its micro-level mechanisms remain insufficiently explored. Drawing on data from the 2014–2022 China Family Panel Studies (CFPS), this study matches household-level data with provincial digital trade development metrics and employs a Logit fixed-effects model to assess the impact of digital trade on clean energy usage. Results reveal that digital trade significantly increases the likelihood of households adopting clean energy, primarily through enhanced accessibility, improved cognition, and technological diffusion. These findings remain robust across a suite of tests, including hierarchical linear model, fixed-effect adjustments, province-year interaction terms, random-effects models, and double machine learning. Heterogeneity analysis further shows the effect is more pronounced among households with higher social status, lower regional economic development, and those in rural areas. This study empirically uncovers the behavioral transmission channels through which digital trade advances green transitions, offering theoretical insight and policy guidance for integrating digital economies with clean energy agendas.https://doi.org/10.1088/2515-7620/ade1abdigital tradehousehold energy consumption structurelogit fixed effectsheterogeneity analysis |
| spellingShingle | Shi Liang Hongliang Li Unpacking the green potential of digital trade: evidence from household energy patterns Environmental Research Communications digital trade household energy consumption structure logit fixed effects heterogeneity analysis |
| title | Unpacking the green potential of digital trade: evidence from household energy patterns |
| title_full | Unpacking the green potential of digital trade: evidence from household energy patterns |
| title_fullStr | Unpacking the green potential of digital trade: evidence from household energy patterns |
| title_full_unstemmed | Unpacking the green potential of digital trade: evidence from household energy patterns |
| title_short | Unpacking the green potential of digital trade: evidence from household energy patterns |
| title_sort | unpacking the green potential of digital trade evidence from household energy patterns |
| topic | digital trade household energy consumption structure logit fixed effects heterogeneity analysis |
| url | https://doi.org/10.1088/2515-7620/ade1ab |
| work_keys_str_mv | AT shiliang unpackingthegreenpotentialofdigitaltradeevidencefromhouseholdenergypatterns AT hongliangli unpackingthegreenpotentialofdigitaltradeevidencefromhouseholdenergypatterns |