| 摘要: |
| 低碳经济是实现双碳目标的重要手
段，同时也是实现经济高质量发展的关键。
文章基于2011—2022 年陕西省10 个地市的
面板数据，利用熵权法，从能源结构等六个
维度选取指标，对陕西低碳经济发展水平进
行测度，在此基础上运用Dagum 基尼系数、
核密度估计和空间收敛模型，揭示低碳经济
发展水平区域差异来源、动态演变及收敛特
征， 最后采用二次指派程序（Quadratic
Assignment Procedure, QAP）的方法，考察
了陕西低碳经济发展差异的内在机理。研究
发现：第一，陕西省及各区域低碳经济发展
水平呈现出逐年提高态势；第二，区域间差
异是低碳经济发展水平失衡的主要来源；第
三，陕南地区存在σ 收敛特征，陕西、关中
和陕南均存在绝对β 收敛特征；第四，除低
碳产业外，其余各维度发展差异均对低碳经
济发展差异有显著正向作用。最后根据实证
研究结果，整体把握陕西省低碳经济发展水
平及各城市之间的发展差异，为陕西省如何
提高低碳经济发展水平，加快向低碳经济社
会转型提供切合实际的建议。 |
| 关键词: 低碳经济 区域差异 动态演进 收敛性 内在机理 |
| DOI：10.13791/j.cnki.hsfwest.20241023003 |
| 分类号: |
| 基金项目:陕西省社会科学基金项目（2022D016）；西安石油大学研究生创新与实践能力培养计划资助项目（YCS22214296） |
|
| Analysis on regional difference, convergence and internal mechanism of low carboneconomy development level in Shaanxi Province |
|
TANG Juanli,GAO Yuan,NI Yongliang
|
| Abstract: |
| Low-carbon economy constitutes a critical pathway to achieving China’s dual carbon goals
and a core driver of high-quality economic development, integrating environmental sustainability with
long-term economic growth momentum. As a major energy province and a key national
comprehensive energy security base, Shaanxi Province bears unshirkable political responsibilities for
ensuring stable energy supply—its abundant fossil energy reserves and strategic geographical location
make it an indispensable pillar of national energy security, while also imposing the arduous task of
reconciling energy production with low-carbon transition. Based on panel data of 10 prefecture-level
cities in Shaanxi from 2011 to 2022, this study constructs a comprehensive evaluation index system
for low-carbon economic development, incorporating 33 basic indicators across six interconnected
dimensions: energy structure, low-carbon industry, low-carbon innovation, low-carbon society, lowcarbon
environment, and economic development. The entropy weight method is adopted for objective
evaluation, as it quantifies indicator weights based on information entropy without subjective biases,
ensuring the reliability of the low-carbon development level measurement. On this basis, three
quantitative methods are employed to explore regional characteristics: the Dagum Gini coefficient and
its decomposition method to identify sources of regional disparities; kernel density estimation to
visualize the dynamic evolution of development level distributions; and two classic spatial
convergence models to test long-term trends in regional disparity. Finally, the Quadratic Assignment
Procedure (QAP) is utilized to examine the intrinsic mechanisms of regional differences, leveraging
its advantage in addressing spatial dependence in cross-sectional data. The empirical findings are as
follows first, from 2011 to 2022, the low-carbon economic development level of Shaanxi Province
as a whole, its three major regions, and all 10 prefecture-level cities exhibited a steady upward trend.
This progress is attributed to the implementation of national low-carbon strategies, regional industrial
structure optimization, and increased investment in energy conservation and environmental
protection. Notably, however, unbalanced development persisted across regions and cities, stemming
from disparities in resource endowments, industrial bases, technological capabilities, and policy
implementation effects. Secondly, the overall regional disparity in Shaanxi’s low-carbon economic
development showed a gradual expanding trend. Decomposition results of the Dagum Gini coefficient
indicate that inter-regional disparity is the primary source of overall imbalance, as divergent
development conditions and resource allocation across regions lead to heterogeneous growth
trajectories. Among these, the gap between Guanzhong and Southern Shaanxi is particularly
prominent, reflecting significant differences in economic foundation, technological innovation
capacity, and industrial transformation speed between the two regions. Thirdly, regarding convergence
characteristics: σ -convergence is absent in Shaanxi as a whole, Guanzhong, and Northern Shaanxi,
meaning the relative gap in low-carbon development among cities in these regions did not narrowover time. In contrast, Southern Shaanxi exhibits significant σ-convergence, driven by coordinated development policies and ecological protection efforts that
reduce intra-regional disparities. Furthermore, Shaanxi Province, Guanzhong, and Southern Shaanxi all demonstrate absolute β-convergence, indicating lagging
cities have faster low-carbon development growth rates and the potential to catch up with leading cities over time. Guanzhong’s convergence speed is
significantly faster than that of Southern Shaanxi, primarily due to its advantages in talent aggregation, technological spillover, and industrial agglomeration,
which accelerate the diffusion of low-carbon technologies and models. Fourth, QAP analysis results reveal that except for low-carbon industry, development
disparities in the other five dimensions exert a significant positive impact on low-carbon economic development gaps. Among these factors, low-carbon
innovation has the strongest effect, underscoring that the uneven distribution of innovation resources is the key driver of regional disparities. |
| Key words: low-carbon economy regional differences dynamic evolution convergence intrinsic mechanism |
