デジタル技術革新は炭素排出の公平性を促進するか：中国地級市の証拠（論文データ）

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a replicable framework combining Theil-based carbon inequality indices with patent-text digital innovation measures.

🏢実務担当者:Limited direct use, but the city-level carbon disclosure index construction may inform regional benchmarking.

🏛政策担当者:Suggests digital innovation policy may interact with carbon-equity outcomes, relevant to regional decarbonization targeting.

Digital technology innovation is an important engine for the high-quality development of China's economy. However, can this emerging technology promote the fairness of carbon emissions across all Chinese cities? This article measures the carbon emission inequality index from three dimensions: interpersonal carbon equity, ecological carbon equity, and economic carbon equity. It obtains the digital technology innovation indicators through text analysis of patent data and empirically examines the impact of digital technology innovation on carbon emission equity. The data of this paper includes:1. Carbon Emission Inequality IndexThe inequality in carbon emissions mainly stems from disparities between regional carbon emission rights and actual carbon emissions. This paper measures regional carbon emission rights from three perspectives—interpersonal, ecological, and economic—and, based on the Theil index, calculates the distributional disparities between county-level carbon emissions and county-level total population, ecological carbon sequestration capacity, and economic output. These are used to construct the prefecture-level interpersonal carbon inequality index, ecological carbon inequality index, and economic carbon inequality index, respectively [1].2. Digital Technology InnovationDrawing on existing literature (Wu et al. 2021; Chen et al. 2024; Huang B, et al. 2023), this paper classifies digital technologies into five major categories: artificial intelligence, big data, cloud computing, blockchain, and 5G. On this basis, text analysis is conducted on the application document data of all invention and utility model patents obtained from the IncoPat patent database. Patent data containing digital technology keywords are extracted and aggregated by the prefecture-level city of the patent applicant to obtain the total number of digital technology patents at the prefecture level. This total is then incremented by one and natural-log transformed to serve as the digital technology innovation variable in this paper.3. Other VariablesTo account for the potential impact of other factors on the robustness of the empirical results, this paper selects a series of control variables, including carbon emission level (natural logarithm of carbon dioxide emissions), ecological carbon sequestration capacity (natural logarithm of net primary productivity), level of economic development (natural logarithm of real GDP with 2004 as the base year), urbanization rate (year-end urban permanent resident population divided by total permanent resident population), industrial structure (share of value added by the tertiary industry), level of fiscal expenditure (natural logarithm of fiscal expenditure), and degree of opening-up (natural logarithm of total imports and exports). In the mechanism analysis, this paper selects green technology innovation, total energy consumption, economic development gap, and carbon information disclosure index as mechanism variables [2]. To exclude the impact of extreme outliers on subsequent results, all variables used in this paper are winsorized at the 1% level. Taking into account changes in administrative divisions during the study period, missing variable data, and the fact that some prefecture-level cities are not divided into districts and counties, the final research sample is determined to be panel data for 281 cities (excluding Xizang, Hong Kong, Macao, and Taiwan) from 2004 to 2024.[1] The original data on county-level total carbon emissions, total population, and net primary productivity are sourced from the Annual gridmaps dataset of the European Union’s Emissions Database for Global Atmospheric Research (EDGAR), the LandScan Global dataset released by the Oak Ridge National Laboratory (ORNL) of the U.S. Department of Energy, and the MOD17A3HGF dataset in the Earth Science Database developed by the National Aeronautics and Space Administration (NASA), respectively. County-level GDP data are compiled from the statistical yearbooks and local statistical bulletins of each district and county.[2] Green technology innovation is measured by the natural logarithm of the number of invention and utility model patents classified under CPC class Y02 technologies jointly issued by the European Patent Office (related to technologies for mitigating and adapting to climate change), incremented by one. Energy consumption is measured by the logarithm of the total standard coal equivalent of electricity, coal gas and natural gas, and liquefied petroleum gas. The economic development gap is measured by the Theil index of per capita GDP among districts and counties within a prefecture-level city. The prefecture-level city carbon information disclosure index is calculated based on the corporate carbon information disclosure index from the CSMAR database. The CSMAR database publishes the carbon disclosure index for Chinese listed companies from 2008 to 2024, with a higher index indicating a higher level of corporate carbon information disclosure. First, the mean corporate carbon information disclosure index is calculated at the provincial and prefecture-level city levels, respectively, and then the two are summed to obtain the carbon information disclosure index at the prefecture-level city level.

• scidb https://doi.org/10.57760/sciencedb.018mkfirst seen 2026-11-13 06:28:16 · last seen 2026-11-14 06:25:20