Home ›
Entities
› academia
› Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm
Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm
Research article (Frontiers in Environmental Science, 2024) · cited 18× · AI/ML
Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm
Summary
Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm is a scholarly article[1].
Key Facts
Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm. Retrieved May 24, 2026, from https://4ort.xyz/entity/spatial-temporal-evolution-characteristics-and-driving-factors-of-carbon-emission-prediction-in-china-research-on-arima-
MLA“Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/spatial-temporal-evolution-characteristics-and-driving-factors-of-carbon-emission-prediction-in-china-research-on-arima-.
BibTeX@misc{4ortxyz_spatial-temporal-evolution-characteristics-and-driving-factors-of-carbon-emission-prediction-in-china-research-on-arima-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm}}, year = {2026}, url = {https://4ort.xyz/entity/spatial-temporal-evolution-characteristics-and-driving-factors-of-carbon-emission-prediction-in-china-research-on-arima-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm — https://4ort.xyz/entity/spatial-temporal-evolution-characteristics-and-driving-factors-of-carbon-emission-prediction-in-china-research-on-arima- (retrieved 2026-05-24)
