The burgeoning field of digital humanities has provided important modern technological means for text mining in literary works. Chinese classical poetry, as a treasure in the world’s artistic treasury, holds significant value in recognizing the heritage of world culture. In this study, taking the 1589 Tang poems from the Tang Poetry Road in Eastern Zhejiang as an example, we constructed a research framework that explores the aesthetics of classical Chinese poetry landscapes and spatial imagery at the urban agglomeration scale by utilizing geographic and analytical tools such as Python programming, Gephi co-occurrence semantic networks, and GIS kernel density analysis. The framework exhibits three key innovations: (1) a text processing approach that treats individual characters as semantic units in ancient poetry texts, (2) a combined approach of Python programming techniques and Gephi visualization tool for social network analysis, and (3) a study focusing on the integration of textual and spatial aspects of literary landscape heritage corridors at the urban cluster scale. The constructed framework greatly enhances the efficiency and accuracy of Tang poetry text mining, it enables the extraction of natural and cultural landscape spatial imagery along the Tang Poetry Road, the construction of scene depictions, the identification of key regions within the scenes, and the derivation of location-specific strategies. This study broadens the scope of exploring the cultural heritage value of Tang poetry literature and provides practical guidance for the development of cross-regional heritage corridors.

Introduction: Global climate change, increase in human activities, and prominence of ecological issues have led to uneven quantitative and spatial distributions of carbon emission and sequestration of terrestrial ecosystems. Such uneven distributions can lead to more negative impacts on the natural environment and human living conditions.Methods: Therefore, based on the carbon neutralization policy, we conducted geographically weighted regression (GWR) modeling in this study using panel data from 352 Chinese prefectural administrative districts in 2000, 2005, 2010, and 2017 to analyze and determine the impact factors and their spatial distribution for carbon emission and sequestration of terrestrial ecosystems.Results: Our results showed that total population (TP), per capita gross domestic product (GDP) (PCG), proportion of secondary industry output (PSIO), scale of urban built-up area (SUB), green space proportion in city areas (GSP), normalized difference vegetation index (NDVI), and temperature (TEM) are factors driving carbon sequestration and carbon emission. The spatial distribution of these driving factors in mainland China is: (1) TP showed a negative correlation to carbon emission in most areas, while it exhibited a positive correlation to carbon sequestration in the southern, southwestern, and western parts of northwest China; however, in all other areas, TP showed a negative correlation with carbon sequestration; (2) PCG was positively correlated to carbon emission in most areas of China and to carbon sequestration in southwest, south, central, and northeast China; however, PCG demonstrated a negative correlation to carbon sequestration in the remaining areas; (3) PSIO and SUB presented a positive correlation to carbon emission and a negative correlation to carbon sequestration in most areas; (3) In contrast, GSP showed a negative correlation to carbon emission and a positive correlation to carbon sequestration in most areas; (5)NDVI showed a negative correlation to carbon emission and carbon sequestration in most areas toward the east of the “Heihe-Tengchong Line”; NDVI was positively correlated to both carbon emission and sequestration toward the west of this line; (6)TEM was positively correlated to carbon emission and sequestration in most parts of China.Discussion: Based on these results, we further divided the Chinese cities into 6 groups: (1) Groups 1, 2, 3, and 6 are areas where carbon emission and sequestration are governed by both socioeconomic and natural ecological factors. The major driving factors of carbon emission and carbon sequestration in group 1 are PSIO, GSP, and NDVI; the driving factors of group 2 are SUB and NDVI. Meanwhile, carbon emission and sequestration in group 3 are governed by PCG, GSP, and NDVI; for group 6, carbon emission and sequestration are controlled by PCG, SUB, GSP, and NDVI; (2) Group 4 represents areas where carbon emission and sequestration are majorly impacted by PCG and SUB, thereby rendering socioeconomic factors as the major driving forces. Group 5 represents areas where carbon emission and sequestration are sensitive to the natural environment, with GSP and NDVI being the driving factors. Considering the uneven distribution of carbon sequestration and emission and the diverse driving factors in different areas of China, we provided guidance for future environmental policies aimed at reducing the uneven distribution of carbon sequestration and emission in different areas to achieve carbon neutralization.