Provincial Scale Research Articles

Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China

BackgroundConducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China.ResultsBased on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons.ConclusionsChina’s provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity.

Spatial Characteristics and Driving Forces of the Water Footprint of Spring Maize Production in Northern China

Using the water footprint (WF) approach to evaluate the water-use efficiency in agricultural production is crucial for assessing the sustainable use of water resources and mitigating water scarcity and pollution. This study calculated the blue, grey, green and total water footprints of spring maize production in Northeast China in 2019 and 2020 and compared the water footprint values at the provincial and municipal scales. In addition, this study analyzed the spatial variation and drivers of the water footprint. The results show that the average water footprints of spring maize production in Northeast China in 2019 and 2020 were 1.78 m3kg−1 and 2.00 m3kg−1, out of which the grey water footprint contributed the most, accounting for 55.19% and 49.85% of the total water footprint, respectively, while the blue water footprint contributed the least, accounting for only 17.44% and 18.68% of the total water footprint. At the provincial level, the water footprint of spring maize production in Northeast China was spatially clustered, with the lowest total water footprint in Heilongjiang Province and the highest total water footprint in Jilin Province. The spatial distribution difference of the spring maize unit yield was the fundamental factor explaining the difference in the water footprint. The precipitation, surface water resources, average temperature, effective irrigated area and the proportion of effective irrigated area also had impacts on the water footprint. This study provides a scientific basis for optimizing the distribution of spring maize production in Northeast China, formulating appropriate sustainable water resource management plans, improving water-use efficiency and realizing sustainable water resource management in Northeast China.

Machine learning modeling of water footprint in crop production distinguishing water supply and irrigation method scenarios

Crop water footprint (WF) calculation via physical process-based crop or hydrological models has basic requirements in terms of cost, time, and knowledge threshold. It is unsuitable for laypersons such as policymakers or cross-disciplinary researchers. Here, machine learning (ML) is an effective solution owing to low cost, high speed, and low processing difficulty. However, the feasibility and applicability of ML modeling for crop WFs at multiple spatial scales remain unverified. Here, we quantitatively evaluated the applicability, optimal model parameters and importance of the feature variables of four ML methods, including random forest (RF), light gradient boosting machine (LightGBM), generalized additive model (GAM), and artificial neural network (ANN), for constructing WF models for crop production at site and provincial scales. The models were tested through the case study for major grain crops (wheat, maize, soybean, and rice) in China over 2000–2019, distinguishing between irrigated and rainfed crops and between different irrigation methods. The findings show that the WF simulation accuracy for the irrigated scenario was higher than that for the rainfed scenario. The type of feasible ML model varied with crop types, variable combinations, water supply scenarios, and spatial scales. At the site scale, the RF model was generally suitable for simulating WF of maize, soybean, and rice, with R2 values ranging from 0.7 to 0.78 under different irrigation scenarios; the LightGBM was more suitable for simulating wheat with R2 values ranging from 0.76 to 0.82. The LightGBM was deemed to be the best model at the provincial scale, with some regions presenting a 50% lower root mean square error in the crop WF simulation than in RF. Variables related to spatio-temporal class feature were found to be the most important in both the RF and LightGBM simulations. This study provides a global methodological reference for different countries or regions to establish methods for rapid WF assessments in crop production with cross-disciplinary researchers or macro policymakers as the target users.

Understanding the impact of interprovincial trade on forest resources in China

Intensified interprovincial trade causes timber flows between regions in China, exacerbating the contradiction between economic development and forest conservation. To understand the impacts of interprovincial trade on forest resource depletion in China, this study developed a timber-extended multiregional input–output model to quantify the virtual timber harvest volume (THV) of each province. A forest stress index (FSI) is also developed to reflect the pressure of forest resource protection in different regions and evaluate the impacts of interprovincial trade on forest stress at national and provincial scales. The results showed that provinces within main forest zones (e.g., Guangxi, Guangdong, and Fujian) presented higher production-based THV than consumption-based THV while the opposite was true for provinces in the rest of the country (ROC) (e.g., Shanghai, Shandong, and Jiangsu). Interprovincial trade in China resulted in 36.86 million m3 of virtual timber flow annually (2008–2017 average), approximately 16.80% of the country's total production-based THV. During the study period, trade activities continuously reduced China's FSI, but ROC provinces mitigated their forest stress at the cost of increasing the burden on provinces in forest zones. Additionally, distant trade tended to have a stronger effect on provincial forest stress compared with trade between adjacent provinces. The findings identified the critical regions and trade routes that directly or indirectly drove forest degradation and revealed the effects of interprovincial trade on forest stress. Furthermore, this study proposed potential policy measures to promote the sustainable management of forest resources in China.

Dryspells and Minimum Air Temperatures Influence Rice Yields and their Forecast Uncertainties in Rainfed Systems

Crop yield predictions on inter-seasonal to inter-annual horizons are subject to a diverse set of uncertainties associated with climate forecast scenarios. However, the uncertainties associated with climate-related parameters can be marginally controlled (reduced) over the growing season, i.e., from planting through harvest, leveraging a suite of climate forecast ensembles as forcings. In this study, we present a novel approach that combines a coupled hydrologic-crop modeling framework with probabilistic forecasts to characterize and reduce uncertainties in seasonal rice yields predictions. By weighting real-time (nowcast) and forecast climate forcings, the comprehensive framework accurately quantifies uncertainties associated with climate forecasts. At a provincial scale, the crop model extensively captured the uncertainties in yields whilst significantly complementing observations over the growing season. We observed that the spread of yield predictions gradually decreased over time (i.e., towards harvest) as subsequent timeframes incorporated a higher degree of present conditions/forcings and reduced reliance on forecasts. Furthermore, we investigated the information exchange between yields and hydrologic/drought variables over different time frames within the season. Notably, we found a higher synchronization of information transfer between yields, dryspells, and minimum air temperatures towards the end of season, indicating strong explicit links between these variables and crop yields. These outcomes have significant implications on crop yield forecasting and nowcasting, particularly in data-poor regions. By providing a better understanding of the uncertainties associated with seasonal climate forecasts and the interplay between hydrologic variables, drought conditions, and crop yields, this research can aid in improving decision-making processes related to agricultural planning, and risk management. Moreover, these insights can inform assessments of economic, social, and environmental impacts of drought in agricultural systems.

The Empress Group in Alberta, Canada

Basal gravel and sand mantling the bedrock floors of buried valleys throughout the Canadian Interior Plains, and conformably overlying proglacial lacustrine sediment, comprise the Empress Group. While previously conceptualized as stratigraphically equivalent deposits of preglacial rivers prior to the first and most extensive continental and montane glaciations, subsequent stratigraphic studies indicated that buried valley basal gravel must have been deposited between, or during, progressively more extensive continental glaciations and could not be stratigraphically equivalent throughout the buried valley network. However, in the general absence of formation-rank stratigraphic description of basal gravel units that might better inform the geologic history of the deposits, most workers simply consider Empress Group sediments time-transgressive. In this paper, we examine basal gravel at provincial and regional scales to understand its genesis and geologic history. At the provincial scale, we map basal gravel in three dimensions using a novel machine learning approach. At the regional scale, we formally define basal gravel formations at either end of the largest buried valley system in Alberta, which informs its glacial history and physiographic development and shows the importance of formation-rank stratigraphic description. Our results indicate that the buried valley network across Alberta is palimpsest in genesis and basal gravel units within it are chronostratigraphically intercalated between tills. We advocate that the Empress Group definition be extended across Alberta with modifications to improve its clarity and utility, and formally define the Old Fort, Unchaga, Ipiatik, and Winefred formations as part of the Empress Group.

Construction and Application of a Water Resources Spatial Equilibrium Model: A Case Study in the Yangtze River Economic Belt

The Yangtze River Economic Belt, as crucial component of China’s “T-shaped” strategy for territorial development and economic layout, has been challenged by the unbalanced spatial distribution of water resources, which has seriously affected high-quality development in harmony with the social economy and ecological environmental protection. In this study, we aim to enhance the conceptual definition of water resource spatial equilibrium. Additionally, we propose a water resource spatial equilibrium evaluation model based on a variable set and partial connection number. This model effectively addresses the limitations of traditional methods by incorporating fuzzy indices and dynamic information, which have previously been overlooked. The spatiotemporal characteristics and future evolutionary trend of water resource spatial equilibrium were analyzed in 11 provinces and 110 cities in the Yangtze River Economic Belt from 1999 to 2018. The results showed that the conceptual definition of water resource spatial equilibrium involves the water resource endowment, water resource development, water resource utilization, water resource supply and demand, water resource matching, and water resource protection. The water resource spatial equilibrium in the 11 provinces gradually improved following a temporal trend; in terms of the spatial trend, the south was better than the north and the west was better than the east. These provinces were sorted as follows: Yunnan > Sichuan > Zhejiang > Jiangxi > Hunan Province > Guizhou > Hubei > Chongqing > Anhui > Jiangsu > Shanghai. The evolutionary trend increased except in Yunnan. The water resource spatial equilibrium of the 110 cities showed that the spatial trends of the three major urban agglomerations were much better than in the other regions, and the temporal trend steadily improved. The 11 provinces and 110 cities could be divided into three and five categories, respectively, according to their spatiotemporal trends. City-scale research on water resource spatial equilibrium can effectively identify and optimize the control area compared with using a provincial scale. When the control targets were set to 20%, 40%, 60%, and 80%, the proportion of the administrative area based on the city scale decreased by 1.20%, 4.99%, 10.52%, and 19.05%, respectively.

Spatial Distribution of Soil Heavy Metals and Regional Control Strategies in China at Province Level

It is important to understand the spatial distribution characteristics and health risks of soil heavy metals for the implementation of soil pollution control measures in different levels and regions. Based on the data of 706 core studies in the last 20 years, the spatial distribution characteristics, accumulation degree, and health risks of soil heavy metals in China were analyzed at the provincial level. The results showed that the soil heavy metals had obvious spatial differences on the provincial scale, with an overall trend of "high in the south and low in the north and high in the east and low in the west." The content of heavy metals in the soil of agricultural land and construction land was high, and the rate of exceeding the standard was higher than that of other land types. Soil heavy metal concentrations in most areas of China were higher than the regional background values and were highly cumulative. The accumulation indices were:Cd(1.80)>Pb(0.23)>Cu(0.17)>Zn(-0.05)>As(-0.56)>Cr(-0.69), with more than 85% of the provincial soils reaching moderate levels of Cd pollution. Non-ferrous metal resource-based provinces such as Yunnan, Guizhou, Guangxi, Hunan, and Jiangxi generally had higher soil heavy metal levels than those in other provinces, and local children faced higher cancer risks. Soil pollution in coastal areas such as Fujian, Zhejiang, Jiangsu, and Tianjin mainly originated from industrial production and urbanization construction. High intensity agricultural utilization was an important cause of soil heavy metal accumulation in Henan, Shandong, and Anhui.

Regionalized life cycle inventory data collection and calculation for perennial forage production in Canada: methodological best practices and limitations

Abstract Purpose In Canada, 95–99% of produced forages are consumed domestically each year, mainly by beef cattle. Despite their importance, their contribution to the Canadian livestock industry and associated ecosystem services has not been investigated. This study developed a life cycle inventory (LCI) of perennial forage production in Canada averaged from 2009 to 2018. Methods LCI data were sourced or calculated from up-to-date, regionally resolved sources and models. Inputs to perennial forage production included the following: concrete, steel, and plastic usage; machinery fuel consumption; electricity, natural gas, and water use for irrigation; and synthetic and organic fertilizer, lime, and herbicide use. Assessed emissions included ammonia and nitrous oxide (N2O); carbon dioxide from energy use; herbicide, nitrate, and phosphate losses; and soil carbon accumulation. Results were expressed per metric tonne of harvested perennial forage dry matter at provincial and regional scales—Western Canada [British Columbia (BC), Alberta (AB), Saskatchewan (SK), Manitoba (MB)] and Eastern Canada [Ontario (ON), Québec (QC)]. Results and discussion Rates of inputs varied, with generally lower nutrient but higher herbicide application in West vs. East. Irrigation was highest in BC, followed by AB and SK; energy consumption was highest in BC and lowest in QC. Higher N2O losses and nutrient losses via leaching and runoff in the East were partially due to greater soil moisture. Although total harvested perennial forage area declined from 6.43 to 5.23 million hectares from 2009 to 2018, these lands continued to accumulate soil carbon. The time period used to calculate average yields affected LCI estimates, as prairie yields were lower 1994–2003/1999–2008 due to drought. Furthermore, soil carbon sequestration estimates were affected by the annual change coefficients employed, underscoring the need for careful interpretation of LCI outputs. Results were compared to other studies and highlighted the importance of the choice of data and methods in creating LCI, and the need for transparency. Conclusions This first national LCI of perennial forage production in Canada provides a baseline for LCI inputs and outputs associated with this sector, highlighting provincial and regional differences. Outputs can be used to conduct future life cycle assessments to assess the environmental impacts of forage production and generate recommendations to improve sustainability, and for education and marketing purposes. This study demonstrates methodological best practices for LCI data mining and calculations, within available data and model limitations, thereby identifying gaps and providing a roadmap for other countries or sectors to develop detailed forage LCI.

Optimizing the structure of food production in China to improve the sustainability of water resources

The conflict between the growing demand for food and limited water resources is intensifying. To further elucidate the relationship between food and water, we construct a water footprint life cycle assessment framework for food products and propose a modified algorithm for measuring a food's water footprint to assess the virtual water transfer between grain crops and animal products. To address the mismatch between regional water resources and food production, we propose a novel optimization model for food production structure, with both reducing water use and maintaining food security as its objectives. Using 2020 as an example, the analysis proposes an adjusted food production structure for China at national, regional, and provincial scales. The results show that 24.9 % of water consumed by grain crops is transferred to animal products through feed grain. The total water footprint of food production in China is 820.8 billion m3, with the blue water footprint accounting for 32.9 % of that total. The blue water footprint for food production in northern China is 161.8 billion m3, which is much larger than 108.2 billion m3 in southern China. Water scarcity is also greater in northern regions, which produce the majority of grain and animal products. Our optimization shows that a reasonable food production structure can balance water resources and food security by remarkably reducing China's total blue water footprint and increasing food production in the south while reducing production in certain northern provinces to ensure sustainable regional development.

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the “NPP-VIIRS-like” nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran’s I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of “several”; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran’s I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. “High–High” and “Low–Low” were the main types of local spatial autocorrelation, and the number of counties with “High–High” agglomeration increased significantly, while the number of counties with “Low–Low” agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China.

Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity.

Identifying the Production–Living–Ecological Functional Structure of Haikou City by Integrating Empirical Knowledge with Multi-Source Data

The inefficient use of urban resources and the imbalance of spatial structures make optimizing land use management a top priority in urban environmental management. Traditional land use classification systems that prioritize only natural features while disregarding human activity can result in redundancy and conflicts in urban planning. The Production–Living–Ecological Space (PLES) approach was developed as an integrated method for territorial spatial classification. However, most existing studies on PLES are conducted at provincial scales, largely overlooking fine-scale usage within cities. In addition, the existing concept of PLES has been vaguely defined, resulting in linear and simple identification methods that are not applicable to complex urban environments. To address these issues, this study proposes a method to identify urban PLES based on supervised classification using random forest models, which integrate empirical knowledge and multi-source heterogeneous information. The experiments conducted in Haikou reveal the regional aggregation of living and production spaces and the scarcity of ecological space in the city. Our study proposes a concrete concept of PLES and a method for identifying PLES that can be applied to multiple regions, providing an effective tool for the coordinated management of urban production, living, and ecological environments.

Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data

Abstract Iron is one of the most important basic materials in infrastructure development, spatial and temporal variation characteristics analysis of infrastructure iron stocks is conducive to revealing its distribution and change patterns from different scales, which can provide a scientific basis for sustainable urban development and iron resource management in China. In this article, we first calculated provincial infrastructure iron stock data from 2000 to 2020. Then, fitting equations between nighttime lighting data and infrastructure iron stock are constructed to simulate the spatial distribution of China’s infrastructure iron stock at 500 m resolution from 2000 to 2020. Finally, the spatial and temporal dynamics of China’s infrastructure iron stock is analyzed from four scales: national, regional, provincial, and urban agglomeration. The results show as follows: (1) China’s infrastructure iron stock grew at an average annual rate of 26.42% from 2000 to 2020, with China’s infrastructure iron stock increasing 6.28 times over the 21 years. Construction facilities are the most important part of the infrastructure iron stock, and its share is still increasing. (2) On a regional scale, the high-growth type of infrastructure iron stock is mainly distributed in the eastern region, while the no-obvious-growth type is mainly distributed in the western region. The high grade of infrastructure iron stock is mainly distributed in the eastern region, while the low grade is mainly distributed in the western region. (3) On a provincial scale, the highest share of no-obvious-growth type of infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high-growth type is in Jiangsu. The highest proportion of low-grade infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high grade is in Beijing. (4) In terms of urban agglomerations, the high-growth type of infrastructure iron stock is mainly located in Shanghai–Nanjing–Hangzhou, while the no-obvious-growth type is mainly located in the Middle south of Liaoning. The high-grade infrastructure iron stock is mainly distributed in Shanghai–Nanjing–Hangzhou, while the low grade is mainly distributed in Sichuan–Chongqing.

Provincial-Scale Research on the Eco-Security Structure in the Form of an Ecological Network of the Upper Yellow River: A Case Study of the Ningxia Hui Autonomous Region

Important for promoting the integrated protection and systematic management of mountains, rivers, forests, farmlands, lakes, grasslands, and sandy areas, ecological networks form the backbone of the regional ecological security pattern. An improved morphological spatial pattern analysis coupled with a minimum cumulative resistance model (MSPA–MCR) based on multi-source data was used to study, on a provincial scale, the ecological security pattern of Ningxia, an ecologically fragile region in the upper reaches of the Yellow River in China. The results show the following: (1) A reasonable classification of ecological sources and ecological corridors is key to constructing hierarchical ecological networks. Classifying ecological sources by replacing patch areas with energy factors and identifying the importance of ecological corridors by modifying the gravity model with the energy factors proposed in this paper could improve the rationality of the hierarchical structure division of ecological networks. (2) Grassland as the substrate vegetation type is an important ecological source type in arid and semi-arid ecologically fragile areas, and forests and lake wetlands are the main ecological source types in mountainous areas and oasis areas, respectively. The study area was located in the arid–semi-arid transitional area, with a variety of ecological types, such as mountain, oasis, and desert. Therefore, the complex ecological source types of forest–grassland–wetland appear in some areas. (3) There are 45 ecological patch groups that can be classified as ecological sources in Ningxia, including 10 primary source groups. The number of primary source groups is small, and their spatial distribution is unbalanced. There are two categories of ecological corridors, the river corridor and the mountain corridor, and the network connectivity is poor. (4) The ecological network structure of Ningxia is presented as an ecological security structure consisting of one belt, three screens, three corridors, and five clusters, forming a hierarchical nested ecological network security structure system.

Land use and cover in subtropical East Asia and Southeast Asia from 1700 to 2018

Land-use/cover change (LUCC) has severely disturbed the terrestrial carbon balance in subtropical East Asia and Southeast Asia (SEASA). High-precision, long-term historical land cover (LC) data, and LC spatiotemporal patterns and transitions, exhibit high levels of uncertainty. This study constructed base and potential map containing 18 LC categories by integrating multisource high-resolution remote sensing datasets in 2014; then collected official reports, FAOSTAT, and long-term historical datasets, and used the correction coefficient method to obtain the area time series of forest, cropland, and urban at the provincial scale from 1700 to 2018; Finally, the fraction map of 18 LCs in SEASA at 1 km resolution during 1700–2018 was reconstructed based on a pixel level area calibration method, and the spatiotemporal patterns and transitions of LUCC were analyzed. Over the past 300 years, SEASA forest area decreased by 145.22 million ha, most rapidly from 1950 to 1980 (sevenfold that of the preceding 250 years). The loss of tropical forest, mainly in Indonesia, Myanmar, and Thailand, accounting for >80% of this decline. Subtropical forest areas increased significantly in China. Cropland and urban areas increased greatly, by 86.55 and 7.97 million ha, respectively, with the largest increases in China, Thailand, Myanmar, Japan, and Indonesia. Other natural land types closely related to human activity declined more, becoming cropland and urban areas, with greater variability. Conversion of forest to other LC types accounted for >70% of the total LC transition from 1700 to 2018. The main sources of cropland were forest (mainly in Thailand, central Myanmar, and southern Vietnam) and other nonforest natural land. Urban growth was mainly via encroachment on forest and other natural land. This study elucidates the spatiotemporal evolution of LUCC in SEASA from 1700 to 2018, and provides historical spatiotemporal data for large-scale bamboo forests, along with high-precision LUCC data for studying terrestrial ecosystem responses to climate change in the SEASA.

Spatio-Temporal Distribution Characteristics of Intangible Cultural Heritage and Tourism Response in the Beijing–Hangzhou Grand Canal Basin in China

The Beijing–Hangzhou Grand Canal is renowned for being one of the longest and largest canals in the world. Running from Beijing to Hangzhou (north to south), it connects China’s five major water systems and has an important impact on the ecological environment and economy of northern and southern China. It also boasts a large quantity of intangible cultural heritage (ICH). Clarifying the spatio-temporal distribution pattern of ICH in the Beijing–Hangzhou Grand Canal Basin and its influencing factors is essential for the protection and utilization of heritage resources and the formulation of management policies. In this study, 977 national ICH items in the Beijing–Hangzhou Grand Canal Basin are analyzed with the help of ArcGIS spatial analysis technology, SPSS regression analysis, and human geography research methods. The results show that the national ICH in the Beijing–Hangzhou Grand Canal Basin has complete categories but varies in provincial scale, particularly between the north and south parts. According to the analysis using tools such as kernel density estimation, standard deviation ellipse, and the center-of-gravity model, it is clear that the ICH in the Beijing–Hangzhou Grand Canal Basin shows different degrees of sub-type aggregation, varying directional characteristics of each batch of ICH, and a centre of gravity of ICH with a tendency to shift in multiple directions. The main factors affecting the spatio-temporal distribution pattern of ICH in the Beijing–Hangzhou Grand Canal Basin are natural geographical factors, socioeconomic factors, and policy environment factors. Moreover, there is a significant positive correlation between ICH resources and the tourism industry that cannot be ignored. This study provides an important reference for planning the reuse of ICH resource systems in northern and southern China.

A quantification and analysis of historical sectoral and regional water withdrawals in Canada

Canada is a water rich country with one of the highest annual water uses per person among developed countries. This study provides a systematic, comprehensive analysis of recent data on this water use at national and subnational scales. It spatially disaggregates surveyed data from Statistics Canada (StatCan) to develop a historical dataset from 2005-2018 for Canadian water withdrawals at provincial and river basin scales for seven water use sectors: domestic use, manufacturing, irrigation, livestock, mining, oil and gas and thermal power generation. Additionally, sectoral water withdrawals are estimated for each province-river basin combination. Sectoral priorities are analyzed at the provincial and river basin scales and historical trends are identified. Water use intensity indicators are calculated and compared between different provinces, and a water stress index is used to identify regions most prone to water shortages. We find that water use decreased nationally over the study years for all sectors except irrigation, mining and oil and gas. Ontario had the highest water use of all provinces, mainly for thermal power generation. Manufacturing and domestic sectors were the dominant users in Quebec and British Columbia while the Prairies had more diversified uses. Domestic water use per capita values in Newfoundland & Labrador and Quebec are higher than the national average and all global values included in the study for comparison. Finally, the irrigation sector withdraws the most water per $GDP nationally while the oil and gas sector withdraws the least. Dataset development faced challenges related to data availability and uncertainties in downscaling assumptions. These challenges are described, and emphasize the need for a systematic and standardized approach to water data gathering and sharing in Canada.

Research on the Characteristics and Influencing Factors of Provincial Urban Network from the Perspective of Local Governance—Based on the Data of the Top 100 Enterprises in Four Categories in Fujian Province

With the development of the division of labor in product value chains and the specialization of urban functions, the network link structure model among cities is being reshaped. Studying the structure of urban networks and its related theories in the context of scale, place and policy is still an open area. This study constructs a research framework to study the urban network formed by the synergy of scale, place and policy. It mainly takes enterprises in different industries in different provinces as the empirical scale and object, and uses methods such as a social network and Geo Detector to analyze the characteristics and influencing factors of the provincial network relationship mode of enterprises among cities. The main findings are as follows. (1) Firstly, the urban network linkage in general shows strong coastal centrality and small-world network characteristics. The urban network linkages reflected by different types of enterprises all have obvious spatial directionality and polarization effects. (2) Coastal cities have strong centrality, and the specialized division of urban functions emerges, with large cities becoming a concentration area for different types of corporate headquarters, while small- and medium-sized cities carry a large number of processing and assembly enterprises. (3) The networks of different types of enterprises have different sensitivity to each influencing factor. For example, emerging industries have the strongest correlation with the economic scale and social services; manufacturing industries are most influenced by the public services, administrative level and development zone level; and service industries are most influenced by science and technology expenditure and the same metropolitan area. In conclusion, this study contributes to the understanding of network heterogeneity at the provincial scale and provides policy support for the local governance scale, as well as promotes the expansion of the urban network theory to network governance applications in the “flow space”.

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

Various land use and land cover (LULC) products have been produced over the past decade with the development of remote sensing technology. Despite the differences in LULC classification schemes, there is a lack of research on assessing the accuracy of their application to croplands in a unified framework. Thus, this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products (i.e., MCD12Q1 V6, GlobCover2009, FROM-GLC and GlobeLand30) based on the harmonised FAO criterion, and quantified the relationships between four factors (i.e., slope, elevation, field size and crop system) and cropland classification agreement. The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency, with overall accuracies of 94.90 and 93.52%, respectively. The FROM-GLC showed the worst performance, with an overall accuracy of 83.17%. Overlaying the cropland generated by the four global LULC products, we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification, respectively. High consistency was mainly observed in the Northeast China Plain, the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain, China. In contrast, low consistency was detected primarily on the eastern edge of the northern and semiarid region, the Yunnan-Guizhou Plateau and southern China. Field size was the most important factor for mapping cropland. For area accuracy, compared with China Statistical Yearbook data at the provincial scale, the accuracies of different products in descending order were: GlobeLand30, FROM-GLC, MCD12Q1, and GlobCover2009. The cropland classification schemes mainly caused large area deviations among the four products, and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products. Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction, which is essential for food security and crop management, so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.