Land-use intensification dominates China's land provisioning services: From the perspective of land system science.

A pressing challenge to global sustainability is meeting the escalating needs of a growing population while safeguarding land resources from degradation. In recent decades, China's rapid growth, expanding population, urban sprawl, and diminishing high-quality farmland have presented a compelling case suitable for exploring solutions and challenges related to this critical issue. Therefore, there is an urgent need for comprehensive and detailed information regarding land systems. Here, we developed the first fine-scale dataset of the China Land System at a spatial resolution of 1 km, covering the period from 2000 to 2015. By leveraging this comprehensive land information, we identified five primary types of land systems and their respective subsystems, thereby delineating distinct patterns of human-environmental interaction. Land system dynamics followed diverse developmental trajectories characterized by incremental shifts toward more functionally centralized systems. Land use intensification played a significant role in increasing the population capacity and food production in China, contributing nearly 93.94% and 84.99%, respectively. In contrast, land cover changes accounted for only 4.69% and 11.43%, respectively. These findings underscore the tendency of previous studies to overestimate the impact of land cover change and underestimate the influence of land use intensification in meeting the growing demands of land-based production. This study emphasizes the importance of transcending traditional land cover-based approaches and integrating land systems into land representation and global land change scenario simulations to promote sustainability.

Mitigation Measures Could Aggravate Unbalanced Nitrogen and Phosphorus Emissions from Land-Use Activities.

Socioeconomic factors and mitigation potentials are essential drivers of the dynamics of nutrient emissions, yet these drivers are rarely examined at broad spatiotemporal scales. Here, we combine material flow analysis and geospatial analysis to examine the past and future changes of nitrogen and phosphorus emissions in China. Results show that anthropogenic nitrogen and phosphorus emissions increased by 17% and 32% during 2000-2019, respectively. Meanwhile, many regions witnessed decreasing nitrogen emissions but rising phosphorus discharged to waterbody, leading to a 20% decrease in the nitrogen/phosphorus ratio. In addition to many prominent factors like fertilizer use, the increasing impervious land area around cities is a notable factor driving the emissions, indicating the urgency to limit building expansion, especially in North China Plain and other less-developed regions. Improving land-use efficiency and consuming behaviors could reduce nitrogen and phosphorus emissions by 65-77% in 2030, but the nitrogen/phosphorus ratio will increase unintendedly due to larger reduction potentials for phosphorus, which may deteriorate the aquatic ecosystem. We highlight that nitrogen and phosphorus emissions should be reduced with coordinated but differentiated measures by prioritizing nitrogen reduction through cropland and food-system management.

How does market-oriented allocation of industrial land affect carbon emissions? Evidence from China.

Industrial land serves as the fundamental basis for urban economic development and significantly contributes to carbon emissions. Effective market mechanisms are crucial for reducing carbon emissions. As such, investigating the impact of market-oriented allocation of industrial land (MAIL) on carbon emissions and its pathways is of substantial practical importance for global low-carbon development. This study constructs a theoretical framework examining the influence of MAIL on carbon emissions, focusing on 285 Chinese cities from 2003 to 2020. The spatial econometric model is employed to analyze the impact of MAIL on carbon emissions. The results show that: first, from a national perspective, MAIL not only reduces carbon emissions within a region but also in neighboring regions. Higher MAIL leads to more effective carbon emission reductions, which are persistent and hysteresis in time. Path analysis demonstrates that MAIL reduces carbon emissions by promoting industrial upgrading and technological innovation. Second, there are differences in the timeliness of carbon emission reduction effects in cities of different scales and regions. For cities of different scales, the carbon reduction effect of MAIL is more stable in large and medium cities compared to megacities and small cities, but in the short term, MAIL will hinder the industrial upgrading of megacities and thus is not conducive to carbon reduction. For different regional cities, the carbon reduction effect of MAIL is more stable in other regions except northeast region, and in the short term, MAIL will inhibit technological innovation in northeast region, which is not conducive to carbon reduction. Consequently, it is essential not only to design a top-level reform plan for MAIL in China but also to establish differentiated reform policies for MAIL, tailored to the unique characteristics of cities with different scales and regions, to effectively reduce carbon emissions.

Future climate imposes pressure on vulnerable ecological regions in China.

Ecological regions of medium fragility account for 55 % of China's land. Large-scale afforestation and land reclamation have been carried out in these areas over the past few decades. However, how future climate change poses risks and challenges to them remains unclear. By establishing a multi-algorithm framework combining machine learning algorithms with multi-source dataset, our work predicts Normalized Difference Vegetation Index (NDVI, a proxy for vegetation greenness) and its variations in the 21st century under different climate scenarios. We find that vegetation greening (i.e., NDVI increase) in northern and southwestern China is unstable over four 20-year periods from 2020 to 2100. However, a strikingly prominent greening is expected to occur on the Qinghai-Tibet Plateau until the end of this century. Future warming can not only exacerbate the difficulties of vegetation conservation and restoration in vulnerable ecological regions, also threaten these new croplands, stymieing ambitions to increase crop production in China. Our results underscore the crucible that a warming climate presents to current restoration projects. We highlight the urgency of adapting to climate change to achieve ambitious goals of carbon sequestration and food security in China.

Saturation response of enhanced vegetation productivity attributes to intricate interactions.

Evidence for the multifaceted responses of terrestrial ecosystems has been shown by the weakening of CO2 fertilization-induced and warming-controlled productivity gains. The intricate relationship between vegetation productivity and various environmental controls still remains elusive spatially. Here several inherent preponderances make China a natural experimental setting to investigate the interaction and relative contributions of five drivers to gross primary productivity for the period from 1982 to 2018 (i.e., elevated atmospheric CO2 concentrations, climate change, nutrient availability, anthropogenic land use change, and soil moisture) by coupling multiple long-term datasets. Despite a strikingly prominent enhancement of vegetation productivity in China, it exhibits similar saturation responses to the aforementioned environmental drivers (elevated CO2 , climatic factors, and soil moisture). The CO2 fertilization-dominated network explains the long-term variations in vegetation productivity in humid regions, but its effect is clearly attenuated or even absent in arid and alpine environments controlled by climate and soil moisture. Divergence in interactions also provides distinct evidence that water availability plays an essential role in limiting the potential effects of climate change and elevated CO2 concentrations on vegetation productivity. Unprecedented industrialization and dramatic surface changes may have breached critical thresholds of terrestrial ecosystems under the diverse natural environment and thus forced a shift from a period dominated by CO2 fertilization to a period with nonlinear interactions. These findings suggest that future benefits in terrestrial ecosystems are likely to be counteracted by uncertainties in the complicated network, implying an increasing reliance on human societies to combat potential risks. Our results therefore highlight the need to account for the intricate interactions globally and thus incorporate them into mitigation and adaptation policies.

Artificial light pollution inhibits plant phenology advance induced by climate warming.

Natural photic regime has been drastically altered by the artificial night sky luminance. Despite evidence of sufficient light brightness inducing plant physiology and affecting phenology, generalization regarding effects of light pollution on plant phenology across species and locations is less clear. Meanwhile, the relative contributions and joint effects of artificial light pollution and climate change or other anthropic stressors still remain unknown. To fill this knowledge gap, we utilized in situ plant phenological observations of seven tree species during 1991-2015 in Europe, night-time light dataset and gridded temperature dataset to investigate the impacts of the artificial light pollution on spatial-temporal shifts of plant phenological phases under climatic warming. We found 70% of the observation sites were exposed to increased light pollution during 1992-2015. Among them, plant phenological phases substantially delayed at 12-39% observation sites of leaf-out, and 6-53% of flowering. We also found plant species appeared to be more sensitive to artificial light pollution, and phenology advancement was hindered more prominently and even delay phenomenon exhibited when the color level showed stronger sky brightness. Linear mixed models indicate that although temperature plays a dominant role in shifts of plant phenological phases at the spatial scale, the inhibitory effect of artificial light pollution is evident considering the interactions. To our knowledge, this study is the first to quantitatively establish the relationship between artificial light pollution and plant phenology across species and locations. Meanwhile, these findings provide a new insight into the ecological responses of plant phenology to the potential but poorly understood environmental stressors under this warmer world and call for light pollution to be accorded the equal status as other global change phenomena.

Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities.

The lockdown of cities against the COVID-19 epidemic directly decreases urban socioeconomic activities. Remotely sensed night-time light (NTL) provides a macro perspective to capture these variations. Here, taking 20 global megacities as examples, we adopted the NASA's Black Marble NTL data with a daily resolution to investigate their spatio-temporal changes. We collected daily NTL products for four weeks (one month) before and after the date of lockdown in each city, which were then summarized as weekly and monthly averaged NTL images after pre-processing (cloud removing, outlier detection, etc.). Results show that NTL overall decreased after the lockdown of cities, but with regional disparities and varying spatial patterns. Asian cities experienced the most obvious reduction of NTL. Particularly, the monthly averaged NTL in Mumbai, India, decreased by nearly 20% compared to one month before. However, there was no significant decline in NTL in European cities. African cities also experienced stable changes of NTL. Spatially, city centers darkened more obviously than the urban periphery. Facing emergencies, NTL data has broad applications in monitoring socioeconomic dynamics and assessing public policies in a near real-time manner.

Estimation of PM2.5 Concentrations in China Using a Spatial Back Propagation Neural Network.

Methods for estimating the spatial distribution of PM2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R2) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R2 values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 µg/m3. The yearly mean PM2.5 concentration in China during the study period was found to be 41.8 µg/m3 and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 µg/m3) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM2.5 concentrations for air quality monitoring.

Towards sustainability? Analyzing changing urban form patterns in the United States, Europe, and China.

Urban form characterizes the spatial structure of fixed elements within a city, which affects daily life and has significant influence on environmental sustainability. Measuring the spatiotemporal characteristics of an urban form and its relationship with sustainable development is the basis of urban planning. Taking 27 large cities in the United States, Europe and China as examples, we developed a ternary graph to quantify urban forms based on the density distribution of the built-up area. The urban forms were divided into the following classes: central-compact, central-sprawl, decentralized-compact, and decentralized-sprawl. Spatially, the cities in the three regions have experienced rapid urban growth, while the urban forms vary greatly from region to region. Urban forms are dominated by decentralized-sprawl in the United States, and central-compact in Europe and China. Temporally, approximately 80% of sample cities kept the urban form class both in 1990-2000 and 2000-2014. It is noted that 40% of sample cities in China tended to grow in a more sprawling pattern in 2000-2014 than in 1990-2000. From the land-saving aspect of urban sustainable development, compact and central spatial growth can significantly reduce per capita land consumption. Population density decreases in all sample cities, but compact and central spatial growth slowed the rate of population density reduction.

Understanding urban expansion combining macro patterns and micro dynamics in three Southeast Asian megacities.

Rapid urbanization accelerates urban expansion, especially in populous areas, such as Southeast Asia. The urban forms and changes at the macro level and the dynamics at the patch level are interrelated. Considering its spatiotemporal interdependences and global-local interactions, we propose a framework to quantify urban expansion by combining macro patterns and micro dynamics. Taking three Southeast Asian megacities, Bangkok, Ho Chi Minh City (HCMC), and Manila, as examples, we calculate the urban land densities in concentric rings (macro pattern) and the proximity expansion index (PEI) of new urban patches (micro dynamic) to compare the urban form changes and expansion patterns based on Landsat imagery in 1990, 2000, and 2014. The results show that the urban form changes have close relationships with the local urban patch dynamics. The macro- and micro-level results in Bangkok and Ho Chi Minh City are interrelated and consistent and the explainable inconsistent results in Manila further reveal the necessity of combination of two scopes. The three megacities developed in different manners, thereby resulting in diverse urban forms and changes. Other methods and technologies combining macro and micro perspectives are encouraged to better understand urban expansion.

PSOLA: A Heuristic Land-Use Allocation Model Using Patch-Level Operations and Knowledge-Informed Rules.

Optimizing land-use allocation is important to regional sustainable development, as it promotes the social equality of public services, increases the economic benefits of land-use activities, and reduces the ecological risk of land-use planning. Most land-use optimization models allocate land-use using cell-level operations that fragment land-use patches. These models do not cooperate well with land-use planning knowledge, leading to irrational land-use patterns. This study focuses on building a heuristic land-use allocation model (PSOLA) using particle swarm optimization. The model allocates land-use with patch-level operations to avoid fragmentation. The patch-level operations include a patch-edge operator, a patch-size operator, and a patch-compactness operator that constrain the size and shape of land-use patches. The model is also integrated with knowledge-informed rules to provide auxiliary knowledge of land-use planning during optimization. The knowledge-informed rules consist of suitability, accessibility, land use policy, and stakeholders' preference. To validate the PSOLA model, a case study was performed in Gaoqiao Town in Zhejiang Province, China. The results demonstrate that the PSOLA model outperforms a basic PSO (Particle Swarm Optimization) in the terms of the social, economic, ecological, and overall benefits by 3.60%, 7.10%, 1.53% and 4.06%, respectively, which confirms the effectiveness of our improvements. Furthermore, the model has an open architecture, enabling its extension as a generic tool to support decision making in land-use planning.