Urban ecosystem modeling and global change: potential for rational urban management and emissions mitigation

Integrating territorial pattern changes into the relationship between carbon sequestration and water yield in the Yangtze River Basin, China

Territorial pattern plays an important role in regional ecosystem management and service provision. It is significant to demonstrate the coordination relationships between the territorial space evolutions and ecosystem services for sustainable regional development. This study focused on quantifying the impacts of production-living-ecological space change on carbon sequestration and water yield in the upper and middle-lower reaches of the Yangtze River Basin. Our results indicated that the production-living-ecological space variation trends are similar between the upper and middle-lower reaches during 2000-2020, while their impacts on ecosystem services are different in their respective regions. In the upper reaches, the changes in production and ecological space had a direct positive impact on NPP while the changes of living space had a negative impact on the NPP. However, the changes of production-living-ecological space had no significant effects on the water yield. In contrast, the changes of production and ecological space had no significant effect on the NPP in the middle-lower reaches, while the changes of ecological space had a positive effect on the water yield. Additionally, we also found that social-economic factors had no significant effects on the changes of ecological space in the middle-lower reaches of the Basin. We suggested that policy makers need to optimize the distribution of territorial space in order to maintain sustainable development.

Decoupling the Effect of Climate and Land-Use Changes on Carbon Sequestration of Vegetation in Mideast Hunan Province, China

Urbanization and global climate change are two important global environmental phenomena in the 21st century. Human activities and climate changes usually increase the uncertainties of the ecosystem functions and structures and can greatly affect regional landscape patterns and the carbon cycle. Consequently, it is critical to understand how various climate and land-use changes influence the carbon dynamics at a regional scale. In this study, we quantitatively analyzed the spatial and temporal changes of net primary productivity (NPP) and the effects of climate factors and human disturbance factors (i.e., land-use changes) on the “Chang–Zhu–Tan” (CZT) urban agglomeration region from 2000 to 2015. The Carnegie–Ames–Stanford Approach (CASA) model was combined with spatially explicit land-use maps, monthly climate data, and MODIS NDVI images to simulate the carbon dynamics in the CZT area. Based on our six different scenarios, we also analyzed the relative roles of climate change and land-use change in total production. Our results indicated that the annual NPP of the study area maintained an upward trend by 7.31 gC•m−2•yr−1 between 2000 and 2015. At the same time, the average annual NPP was 628.99 gC•m−2 in the CZT area. We also found that the NPP was lower in the middle of the north region than in others. In addition, land-use changes could contribute to a positive effect on the total production in the study area by 3.42 T gC. Meanwhile, the effect of climate changes on the total production amounted to −1.44 T gC in the same region and period. Temperature and precipitation had negative effects on carbon sequestration from 2000 to 2015. As forest land constituted over 62.60% of the total land use from 2000 to 2015, the negative effect of carbon sequestration caused by urbanization could be ignored in the CZT area. Although climate and land-use changes had simultaneously positive and negative effects during the period 2000–2015, prioritizing the protection of existing forest land could contribute to increasing carbon sequestration and storage at the regional scale. Our study assists in understanding the impact of climate changes and land-use changes on carbon sequestration while providing a scientific basis for the rational and effective protection of the ecological environment in mid-east Hunan Province, China.

Dynamic metabolism network simulation for energy-water nexus analysis: A case study of Liaoning Province, China

Faced with gradually serious energy and water crisis, the exploration of energy-water nexus metabolism relationships could provide a new direction for achieving the resources conservation and consumption reduction. In this paper, a comprehensive assessment for energy-water metabolic network is proposed to search the dynamic evolution and quantify the sectoral metabolic processes in Liaoning Province, China. Input-output analysis (IOA) and ecological network analysis (ENA) are integrated to clarify the embodied linkages and the complicated system interactions in the energy-water nexus network. The coefficients of "water-related energy" and "energy-related water" are explored through the system dynamics model (SDM) based on the consecutive input-output tables from 2012 to 2018. In addition, energy-water nexus efficiency index (EWEI) is constructed to evaluate the metabolism circulation efficiency of energy-water nexus network. The results indicate that (a) the energy and water outflow sectors mainly concentrate in primary industries and tertiary industries, while inflow sectors mainly concentrate in secondary industries; (b) the sectoral relationship provides effective pathways to implement collaborative resource saving and efficiency improvement measures, and the key sectors with the highest correlation with other sectors in energy-water nexus are AGR (agriculture), MIN (mining) and WRC (wholesale, retail and catering services). (c) the EWEI shows a gradually downward trend and the state of the energy system would determine the health of the energy-water nexus metabolism more strongly than water system.

The state factor model and urban forest restoration

A ‘state factor’ model of ecosystems can serve as a conceptual framework for researching and managing urban ecosystems. This approach provides alternative goals and narratives to those derived from historically grounded dichotomies between nature and culture, which can reify constructions of human influence as inherently destructive. The integration of human behaviour and state factors is critical to the application of a state factor model to urban ecosystems. We emphasize the role of culture in co-producing urban ecosystems and the importance of feedbacks between urban ecosystems and state factors. We advocate for ecosystem models that encourage local agency and actions that enhance the capacity of cities to constructively adapt to environmental change. We contrast this approach to efforts intended to minimize human impacts on ecosystems. The usefulness of the state factor model for informing such efforts is assessed through a consideration of the norms and practices of urban forest restoration in New York City. Despite the limitations and challenges of applying a state factor model to urban ecosystems, it can inform comparative research within and between cities and offers an intuitive framework for understanding the ecological conditions created in cities by human behaviour.

Spatiotemporal Modeling of Urban Growth Using Machine Learning

This paper presents a general framework for modeling the growth of three important variables for cities: population distribution, binary urban footprint, and urban footprint in color. The framework models the population distribution as a spatiotemporal regression problem using machine learning, and it obtains the binary urban footprint from the population distribution through a binary classifier plus a temporal correction for existing urban regions. The framework estimates the urban footprint in color from its previous value, as well as from past and current values of the binary urban footprint using a semantic inpainting algorithm. By combining this framework with free data from the Landsat archive and the Global Human Settlement Layer framework, interested users can get approximate growth predictions of any city in the world. These predictions can be improved with the inclusion in the framework of additional spatially distributed input variables over time subject to availability. Unlike widely used growth models based on cellular automata, there are two main advantages of using the proposed machine learning-based framework. Firstly, it does not require to define rules a priori because the model learns the dynamics of growth directly from the historical data. Secondly, it is very easy to train new machine learning models using different explanatory input variables to assess their impact. As a proof of concept, we tested the framework in Valledupar and Rionegro, two Latin American cities located in Colombia with different geomorphological characteristics, and found that the model predictions were in close agreement with the ground-truth based on performance metrics, such as the root-mean-square error, zero-mean normalized cross-correlation, Pearson’s correlation coefficient for continuous variables, and a few others for discrete variables such as the intersection over union, accuracy, and the f 1 metric. In summary, our framework for modeling urban growth is flexible, allows sensitivity analyses, and can help policymakers worldwide to assess different what-if scenarios during the planning cycle of sustainable and resilient cities.

A Research and Innovation Agenda for Zero-Emission European Cities

The Paris Agreement and SDG13 on Climate Action require a global drop in Green House Gases (GHG) emissions to stay within a “well below 2 degrees” climate change trajectory. Cities will play a key role in achieving this, being responsible for 60 to 80% of the global GHG emissions depending on the estimate. This paper describes how Research and Innovation (R&I) can play a key role in decarbonizing European cities, and the role that research and education institutions can play in that regard. The paper highlights critical R&I actions in cities based on three pillars: (1) innovative technology and integration, (2) governance innovation, and (3) social innovation. Further, the research needed to harmonize climate mitigation and adaptation in cities are investigated.

An urban energy performance evaluation system and its computer implementation

To improve the urban environment and effectively reflect and promote urban energy performance, an urban energy performance evaluation system was constructed, thereby strengthening urban environmental management capabilities. From the perspectives of internalization and externalization, a framework of evaluation indicators and key factors that determine urban energy performance and explore the reasons for differences in performance was proposed according to established theory and previous studies. Using the improved stochastic frontier analysis method, an urban energy performance evaluation and factor analysis model was built that brings performance evaluation and factor analysis into the same stage for study. According to data obtained for the Chinese provincial capitals from 2004 to 2013, the coefficients of the evaluation indicators and key factors were calculated by the urban energy performance evaluation and factor analysis model. These coefficients were then used to compile the program file. The urban energy performance evaluation system developed in this study was designed in three parts: a database, a distributed component server, and a human-machine interface. Its functions were designed as login, addition, edit, input, calculation, analysis, comparison, inquiry, and export. On the basis of these contents, an urban energy performance evaluation system was developed using Microsoft Visual Studio .NET 2015. The system can effectively reflect the status of and any changes in urban energy performance. Beijing was considered as an example to conduct an empirical study, which further verified the applicability and convenience of this evaluation system.

Changing Urban Carbon Metabolism over Time: Historical Trajectory and Future Pathway

Cities are expected to play a major role in carbon emissions mitigation. A key step in decoupling urban economy from carbon emissions is to understand the full impact of socioeconomic development on urban metabolism over time. Herein, we establish a system-based framework for modeling the variation of urban carbon metabolism through time by integrating a metabolic flow inventory, input-output model, and network analysis. Using Beijing as a case study, we track the historical trajectory of carbon flows embodied in urban final consumption over 1985-2012. We find that while the tendency of increase in direct carbon emission continues within this time frame, consumption-based carbon footprint might have peaked around 2010. Significant transitions in emission intensity and roles sectors play in transferring carbon over the period are important signs of decoupling urban development from carbonization. Our further analysis of driving factors reveals a strong competition between efficiency gains and consumption level rise, showing a cumulative contribution of -584% and 494% to total carbon footprint, respectively. Projection into a future pathway suggests there is still a great potential for carbon mitigation for the city, but a strong mitigation plan is required to achieve such decarbonization before 2030. By bridging temporal metabolic model and socioeconomic planning, this framework fills one of the main gaps between monitoring of urban metabolism and design of a low-carbon economy.

Study of Urban Energy Performance Assessment and Its Influencing Factors Based on Improved Stochastic Frontier Analysis: A Case Study of Provincial Capitals in China

To improve energy-use sustainability in cities, we proposed a set of urban energy performance assessment indicators and influencing factors based on existing theory and literature. An urban energy performance assessment and influencing factor model was also constructed by the improved stochastic frontier analysis method, and panel data from provincial capitals in China from 2004 to 2013 were considered as an example to carry out an empirical study. Chosen from both endogenous and exogenous perspectives, the urban energy performance assessment indicators and influencing factors take into consideration the capital, labor, energy, urban economic output, urbanization level, population, area, urban climate, and travel selection. Because it considers both random errors and the inefficiency levels of urban productions, the urban energy performance assessment and influencing factor model could reduce the errors caused by two-stage performance assessment and factor analysis, quantify the effects of assessment indicators and influencing factors on urban energy performance, and reflect the actual performance of different cities. Empirical results show that the urban energy performance of provincial capitals in China has been increasing. Chinese provincial capitals also have great potential for energy saving. It was necessary to include energy input as an assessment indicator when evaluating urban energy performance. Population density and urban energy performance showed a negative correlation, but the urbanization rate, temperature index, and household car ownership were positively related to urban energy performance. The urban energy performance of Chinese provincial capitals gradually decreased from east to west. Based on these results, several policy suggestions on urban energy performance development are proposed.

Evaluating the evolution of the Heihe River basin using the ecological network analysis: Efficiency, resilience, and implications for water resource management policy

One of the most critical challenges in the anthropocentric age is the sustainable management of the planet's increasingly strained water resources. In this avenue, there is a need to advance holistic approaches and objective tools which allow policy makers to better evaluate system-level properties and trade-offs of water resources. This research contributes to the expanding literature in this area by examining the changes to system-level network configurations of the middle reaches of the Heihe River basin from 2000 to 2009. Specifically, through the ecological network analysis (ENA) approach, this research examines changes to the system-level properties of efficiency, redundancy, and evaluates the trade-offs to the resiliency of ecosystem water services of the middle reaches of the Heihe River basin. Our results indicate that while the efficiency of the middle reaches has increased from 2000 to 2009 by 6% and 78% more water is released to the lower reaches, the redundancy of the system has also decreased by 6%. The lower level of redundancy, particularly due to the changes in the groundwater body levels, has critical long-term consequences for the resilience of the water ecosystem services of the middle reaches. In consideration of these holistic trade-offs, two hypothetical alternative scenarios, based on water recycling and saving strategies, are developed to improve the long-term health and resilience of the water system.

Tracking Inter-Regional Carbon Flows: A Hybrid Network Model

The mitigation of anthropogenic carbon emissions has moved beyond the local scale because they diffuse across boundaries, and the consumption that triggers emissions has become regional and global. A precondition of effective mitigation is to explicitly assess inter-regional transfer of emissions. This study presents a hybrid network model to track inter-regional carbon flows by combining network analysis and input-output analysis. The direct, embodied, and controlled emissions associated with regions are quantified for assessing various types of carbon flow. The network-oriented metrics called "controlled emissions" is proposed to cover the amount of carbon emissions that can be mitigated within a region by adjusting its consumption. The case study of the Jing-Jin-Ji Area suggests that CO2 emissions embodied in products are only partially controlled by a region from a network perspective. Controlled carbon accounted for about 70% of the total embodied carbon flows, while household consumption only controlled about 25% of Beijing's emissions, much lower than its proportion of total embodied carbon. In addition to quantifying emissions, the model can pinpoint the dominant processes and sectors of emissions transfer across regions. This technique is promising for searching efficient pathways of coordinated emissions control across various regions connected by trade.

Environmental Impacts of China's Urbanization from 2000 to 2010 and Management Implications

Rapid urbanization in China during the first decade of the twenty first century has brought about profound environmental changes at citywide and regional scales. In this paper, we present a comprehensive set of indicators and put forward a new evaluation method for measuring environmental impacts of urbanization from 2000 to 2010. We compared these impacts among 286 cities in mainland China and found that the overall quantity of pollutant discharge decreased as cities became more economically developed during the years 2000-2010. However, larger and denser cities, and wealthier cities in the eastern part of China tended to have larger quantities of pollutant discharge, resource consumption, and changes in land use (i.e., expansion of the built environment). The discharge increase occurred despite these cities having increased their investment in pollution control and construction of municipal environmental infrastructure. The negative impact from the intensity of pollutant discharge (i.e., discharge per unit of economic output) was generally less in more developed cities, although this was not always the case. Some cities, such as resource-based cities and old industrial cities, had both larger quantities of pollutant discharge and greater pollution intensity compared to other types of cities, indicating that environmental impacts did not necessarily decrease with increasing urbanization. The results of this study provide a promising basis for decision-making to reduce the impacts for different types of cities in the decades to come.

Nonzero-Sum Relationships in Mitigating Urban Carbon Emissions: A Dynamic Network Simulation

The "stove-pipe" way of thinking has been mostly used in mitigating carbon emissions and managing socioeconomics because of its convenience of implementation. However, systems-oriented approaches become imperative in pursuit of an efficient regulation of carbon emissions from systems as complicated as urban systems. The aim of this paper is to establish a dynamic network approach that is capable of assessing the effectiveness of carbon emissions mitigation in a more holistic way. A carbon metabolic network is constructed by modeling the carbon flows between economic sectors and environment. With the network shocked by interventions to the sectoral carbon flows, indirect emissions from the city are accounted for under certain carbon mitigation strategies. The nonzero-sum relationships between sectors and environmental components are identified based on utility analysis, which synthesize the nature of direct and indirect network interactions. The results of the case study of Beijing suggest that the stove-pipe mitigation strategies targeted the economic sectors might be not as efficient as they were expected. A direct cutting in material or energy import to the sectors may result in a rebound in indirect emissions and thus fails to achieve the carbon mitigation goal of the city as a whole. A promising way of foreseeing the dynamic mechanism of emissions is to analyze the nonzero-sum relationships between important urban components. Thinking cities as systems of interactions, the network approach is potentially a strong tool for appraising and filtering mitigation strategies of carbon emissions.

Exploring the response of net primary productivity variations to urban expansion and climate change: a scenario analysis for Guangdong Province in China

Urban land development alters landscapes and carbon cycle, especially net primary productivity (NPP). Despite projections that NPP is often reduced by urbanization, little is known about NPP changes under future urban expansion and climate change conditions. In this paper, terrestrial NPP was calculated by using Biome-BGC model. However, this model does not explicitly address urban lands. Hence, we proposed a method of NPP-fraction to detect future urban NPP, assuming that the ratio of real NPP to potential NPP for urban cells remains constant for decades. Furthermore, NPP dynamics were explored by integrating the Biome-BGC and the cellular automata (CA), a widely used method for modeling urban growth. Consequently, urban expansion, climate change and their associated effects on the NPP were analyzed for the period of 2010-2039 using Guangdong Province in China as a case study. In addition, four scenarios were designed to reflect future conditions, namely baseline, climate change, urban expansion and comprehensive scenarios. Our analyses indicate that vegetation NPP in urban cells may increase (17.63 gC m(-2) year(-1)-23.35 gC m(-2) year(-1)) in the climate change scenario. However, future urban expansion may cause some NPP losses of 241.61 gC m(-2) year(-1), decupling the NPP increase of the climate change factor. Taking into account both climate change and urban expansion, vegetation NPP in urban area may decrease, minimally at a rate of 228.54 gC m(-2) year(-1) to 231.74 gC m(-2) year(-1). Nevertheless, they may account for an overall NPP increase of 0.78 TgC year(-1) to 1.28 TgC year(-1) in the whole province. All these show that the provincial NPP increase from climate change may offset the NPP decrease from urban expansion. Despite these results, it is of great significance to regulate reasonable expansion of urban lands to maintain carbon balance.