AI Article Synopsis

  • * Using data from 59 cities in China's Yellow River Basin from 2004 to 2021, the study modifies traditional carbon accounting models to assess the distribution and impact of ecocompensation credits across regions.
  • * The findings reveal that while ecocompensation credits show patterns of agglomeration, their distribution is uneven; the effectiveness of green technology emerges as the main factor influencing these changes, providing insights for ongoing and future ecocompensation policies.

Article Abstract

With the progression of regional connectivity, attaining the 'dual-carbon target' is no longer the narrative of a single city. To achieve synergistic governance among cities in the basin economic zone, it is essential to establish a multidimensional ecological compensation mechanism. This research amended the conventional carbon accounting model by utilising carbon emissions and economic data from 59 cities in China's Yellow River Basin, covering the period from 2004 to 2021, after the timeframe of significant alterations in the administrative divisions of cities was removed. It additionally compares and analyses the clustering patterns and trajectories of centre-of-gravity shifts in regional ecocompensation credits while investigating the impact processes through geo-probes. The findings indicate that the amended carbon account has a more decentralised distribution when interregional disparities are considered. Second, ecocompensation credits are characterised mostly by low-high and low-low agglomeration; nevertheless, their regional distribution is uneven. Moreover, the rankings of the top five cities in terms of ecocompensation exhibited minimal variation each year. The degree of green technology is the primary determinant of the spatiotemporal evolution of ecological compensation. The conclusions of this study can serve as a valuable reference for the execution of medium- and long-term ecocompensation policies and aid in attaining the 'dual-carbon' aim.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11574075PMC
http://dx.doi.org/10.1038/s41598-024-78269-7DOI Listing

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