Low-carbon Scenarios Research Articles

System-driven design of flexible nuclear power plant configurations with thermal energy storage

Nuclear power plants are expected to make an important contribution to the decarbonisation of electricity supply alongside variable renewable generation, especially if their operational flexibility is enhanced by coupling them with thermal energy storage. This paper presents a system modelling approach to identifying configurations of flexible nuclear plants that minimise the investment and operation costs in a decarbonised energy system, effectively proposing a system-driven design of flexible nuclear technology. Case studies presented in the paper explore the impact of system features on plant configuration choices. The results suggest that cost-efficient flexible nuclear configurations should adapt to the system they are located in. In the main low-carbon scenarios and assuming standard-size nuclear power plants (1,610MWel), the lowest-cost system configuration included around 500MWel of additional secondary generation capacity coupled to the nuclear power plants, with 4.5GWhth of thermal storage capacity and a discharging duration of 2.2 h. Net system benefits per unit of flexible nuclear generation for the main scenarios were quantified at £29-33 m/yr for a wind-dominated system and £19-20 m/yr for a solar-dominated system.

Research on Green and Low-Carbon Development Path of the Electric Power Industry

In the analogy where achieving the “carbon peaking and carbon neutrality” goals (the “dual carbon” goals) is compared to a battle, energy is the main battlefield, and electric power the main force. The low-carbon transformation path of the electric power industry exerts fundamental impact on progress towards carbon peaking and carbon neutrality of the whole society. Therefore, this paper first analyzes challenges and opportunities faced by the electric power industry to achieve the “dual carbon” goals; secondly, on the basis of deep low-carbon, zero-carbon and negative-carbon scenarios of the electric power industry, this paper quantifies and delves into the consumption structure of primary energy and final energy, as well as the future development positioning and trend of different categories of energy. Based on the research results, this paper proposes low-carbon transformation path of the electric power system at the stages of carbon peaking, deep low-carbon and carbon neutrality, and analyzes the trend of power supply cost changes in different scenarios. On this basis, a series of implementation key points and measures are proposed to adapt to green and low-carbon transformation of the electric power industry, covering areas such as coal-fired electric power development positioning, new energy development and utilization, diversified supply and demand, new energy industry chain, and power supply costs, and so forth.

Scenario prediction of carbon peaking in China's logistics industry based on SVR

As a strategic, basic and leading industry for the development of China's national economy, the logistics industry shoulders the important mission of implementing the national "dual carbon" goal. It is urgent to keep up with the pace of the times and accelerate the green and low-carbon process of the logistics industry. Based on the relevant data from 1998 to 2020, this paper selects 8 indicators such as fixed asset investment in the logistics industry, freight turnover, passenger turnover, freight volume, the number of trucks, PM2.5 concentration and per capita GDP. The scenario analysis method is combined with the SSA-SVR model to establish a scenario prediction model for China's logistics industry's carbon peaking. The research results show that: under the baseline scenario, the carbon emissions of China's logistics industry will peak in 2047; under the low-carbon scenario, carbon emissions will peak in 2038; under the enhanced low-carbon scenario, carbon emissions will peak in 2029. 55.94 million tons. Combined with the trend analysis results under different scenarios, China should continue to strengthen the construction of the carbon trading market, optimize the industrial structure, and accelerate the progress of carbon peaking.

China's oil and gas demand forecast and pipeline network layout optimization analysis

In order to accelerate the speed of pipeline network construction and expand the scale of oil and gas pipeline network, we need to forecast the total oil and gas demand and regional demand structure, so as to better serve the future construction of national oil and gas network. In this paper, the LEAP model is used to forecast the total oil and gas demand consumption and its regional distribution in 2050. The results show that in the low-carbon scenario, China's oil and gas will decrease in 2035, but still maintain a certain proportion. There are great differences in oil and gas demand among provinces. In the future, the layout of oil and gas pipeline network should consider the changes of demand structure and spatial layout, and comprehensively consider the transportation distance, transportation cost, transaction price and other factors of oil and gas pipeline network, so as to scientifically layout oil and gas transportation pipeline and optimize the flow of resources.

Regional allocation of carbon emission quotas in China under the total control target

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.

Enhancement of underwater shock wave by coupling electrical explosion and al-powder-suspension combustion: Facing advanced reservoir stimulation technology

The development of fossil energy requires eco-friendly and high-efficient approaches, especially in contemporarily low-carbon scenarios. Using the high-power pulsed discharge to induce fractures has drawn increasingly academic and industrial attention in reservoir stimulation. Therein, the electrical wire explosion is commonly adopted as the pulsed discharge load to transfer electrical energy to mechanical one (shock wave and plasma bubble) efficiently. To strengthen mechanical loadings, a discharge capsule with a metallic wire inside Al-powder suspension has been designed and tested. Experiments were performed with a μs-timescale pulsed power source with stored energy from 150 to 750 J, and the electrical explosion were diagnosed via high-speed photography, electrophysical, and mechanical measurements. The experiment shows that there is an optimal concentration of Al-powder suspension in order to obtain the maximum peak pressure of shock wave for a given discharge condition. Too high or low concentrations of the aluminum powder will cause a lower peak pressure. This paper shows that at the stored energy of 500 J, when the concentration of Al-powder suspension is 20 g/L, the peak pressure can reach 4.81 ± 0.27 MPa, which is 2.01 times as large as the shock wave generated by the electrical explosion under the same conditions in pure water. Through the electrophysical measurement, it is found that the aluminum powder almost does not affect the phase transition of the wire. However, in the subsequent plasma formation, the gaseous reaction products make the discharge channel more complex and in a higher pressure density state, which is shown by the increase of channel resistance. Through the backlit images taken by the high-speed camera, it is obvious that two discrete strong shock waves can be seen in the Al-powder-suspension environment.

Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China

It is crucial for China to take the characteristics and development stage of every province in the region into account in order to achieve the “dual carbon” development goal. Using data collected from 2000 to 2019, this study identifies the factors that influence carbon emissions using the logarithmic mean Divisia index (LMDI) method and establishes a revised stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to investigate the effects of four key factors on carbon emissions in Jiangsu province: population size, economic output, energy intensity, and energy structure. The following conclusions were drawn: (1) energy intensity contributes to a slowed rate of carbon emission production in Jiangsu, whereas population size, economic output, and energy structure contribute to a pulling effect; (2) under different scenarios, Jiangsu’s carbon dioxide emissions peak at different times and reach different values; and (3) two low-carbon scenarios are more in line with the current development situation and future policy orientation of Jiangsu Province and are therefore better choices. Our policy recommendations are as follows: (1) the development of economic and social activities should be coordinated and greenhouse gas emissions should be reduced; (2) the province’s energy structure should be transformed and upgraded by taking advantage of the “dual carbon” development model; and (3) regionally-differentiated carbon emission reduction policies should be developed.

Multi-scenario Evolution of Tourism Carbon Emissions in Jiangxi Province under the “Carbon Peak and Neutrality” Target

Regional tourism needs to respond positively to the “carbon peak and neutrality” target, and the key and most difficult aspect is the prediction of carbon emissions. In this paper, the total carbon emissions of the tourism industry in Jiangxi Province from 2000 to 2019 are calculated by using terminal consumption and the tourism development coefficient. The factors influencing the carbon emissions of the tourism industry are studied by means of logarithmic mean weight Divisia index decomposition (LMDI), and the timing of the tourism industry carbon peak is predicted by combining the extensible random environmental impact assessment model (STIRPAT) and scenario analysis method. The results show three key aspects of this system. (1) In the historical period, the carbon emissions of the tourism industry in Jiangxi Province increased from 71.365×104 t in 2000 to 2342.456×104 t in 2019, with an average annual change rate of 21.09%. The scale of tourism investment was the most important factor affecting the carbon emissions of tourism industry in this period. (2) The main factor that will affect the change of tourism carbon emissions in Jiangxi Province in the future is the carbon emission intensity, and its influence coefficient reaches 0.810. The degrees of influence of tourism income, tourism number and tourism investment follow in sequence. (3) The peak time of carbon emissions from tourism in Jiangxi Province varies under different scenarios. In the baseline scenario, it is estimated to be around 2035, and the average annual change rate will be –0.88%. In the medium- and low-carbon scenarios, the peak carbon emissions will be realized around 2030 and 2025, with the average annual change rates being –1.11% and –1.58%, respectively, indicating that the government's low-carbon policy will have an impact on the carbon emission intensity of tourism and promote the tourism industry in Jiangxi Province to advance by 5 to 10 years. This study provides a theoretical basis for allowing regional tourism to achieve its carbon peak in advance, which supports the prediction of the whole country's “carbon peak and neutrality”, and also provides a measurement basis for the realization of carbon neutralization in tourism.

Decoupling relationship between carbon emissions and economic development and prediction of carbon emissions in Henan Province: based on Tapio method and STIRPAT model.

In order to cope with global warming, China has put forward the "30 · 60" plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the "carbon peak" goal by 2030years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of "carbon peaking" and "carbon neutralization" as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.

A study on the transmission of trade behavior of global nickel products from the perspective of the industrial chain

Critical Mineral is the important material basis for achieving carbon neutrality. Under a low-carbon scenario, the demand for nickel in the new energy will grow rapidly, which may lead to supply shortages. Under the Russia-Ukraine conflict, the nickel trade changes frequently and the trade will become more complicated. To clarify the risk transmission mechanism, based on the nickel products trade data of UN COMTRADE, this paper combines econometrics and complex network methods to construct the Global Nickel Industry Chain Trade Behavior Transmission Network (GNICTBTN). The research shows that the Main Transmission System of the GNICTBTN includes 18 countries and 25 trade links, of which 6 trade links need to be paid attention to in the middle and early stages of trade changes, and it indicates that nickel resource is at risk of being monopolized by middlemen. This study distinguishes the industrial chain structure of seven key countries, and finds that the relevance of trade links is closely related to trading transmission risk. According to GNICTBTN, we predict that the Russia-Ukraine conflict will affect the developing countries in the Asia-Pacific region and developed countries in Europe and USA in short term, and repeatedly affects the main transmission system for longer.

Empirical decomposition and peaking path of carbon emissions in resource-based areas

The pre-development of resource-based regions has led to excessive CO2 emissions and caused serious damage to the ecological environment. A crucial step in combating the problem of global climate change is encouraging resource-based regions to complete the transition to a low-carbon economy. In this study, the influence factors of carbon emissions in resource-based regions were analyzed using the Generalized Diagrammatic index model (GDIM) based on the development characteristics of resource-based economy in Liaoning Province. Furthermore, this study conducted a Monte Carlo simulation of the trajectory of carbon emission peaking based on the scenario analysis. The results show that different industries need to develop targeted emission reduction programs according to their own development characteristics. In each industry, output scale and investment carbon intensity were the primary determinants of carbon emissions, whereas output carbon intensity, investment scale, and investment efficiency were the primary limiting variables. Other variables had varying effects on carbon emissions in other industries. If the previous economic development trend and emission reduction policies are followed, Liaoning's overall carbon emissions won't peak until 2030, and there is currently intense pressure on the province to cut emissions. The carbon peak pathway in the low-carbon scenario differs from that in the technological breakthrough scenario, and only low-carbon technology innovation and breakthrough can ensure that Liaoning will meet the carbon peak target before schedule. This article is useful as a source of information for studies on low-carbon sustainable development in resource-rich areas and as a theoretical foundation for Liaoning to meet its carbon peak objective.

Investigating flood exposure induced socioeconomic risk and mitigation strategy under climate change and urbanization at a city scale

Investigating future flood exposure risk and associated mitigation strategies is vital for sustainable urban development. This study coupled hydrological modeling with future climate and urbanization scenarios from Representative Concentration Pathways and Shared Socioeconomic Pathways (SSP), to spatiotemporally investigate the risk of flood exposure and the effectiveness of mitigation strategy at the city scale. A highly urbanized (80% of built-up areas) and densely populated (7500 per km2) region in downtown Chaohu, China was selected as the study case. Results showed that the current hydrological pattern brought 4.64 × 106 m3 of annual runoff and 292.00 l s−1 of peak flow to the given area, thereby causing 41.03% of the population to be exposed to flooding threats and putting 51.17% of Gross Domestic Product (GDP) at risk. Scenario analysis demonstrated that population and GDP exposures to flood increased to at most 61.11 and 71.30% in high-carbon emission scenarios (SSP3-7.0/SSP5-8.5), respectively. The green infrastructure (GI) strategy captured at most 81.43% of runoff volume and 54.53% of peak flow, thereby decreasing population and GDP exposures to at least 18.38 and 33.04% in low-carbon emission scenarios (SSP1-2.6/SSP2-4.5), respectively. According to the uncertainty analysis, population and GDP exposures showed wide uncertainty ranges of 18.38–61.11 and 33.04–71.30% in future scenarios, and the GI strategy effectively decreased the flood exposure uncertainty ranges to 15.56–42.70 and 17.35–43.60%, respectively. In addition, the spatial analysis indicated that the population- and GDP- intensive regions were more vulnerable to flood exposure than other regions, while GI strategy in these regions showed limited effectiveness in mitigation of exposure risk. The data of this study could assist sustainable urban development in response to climate change and urbanization.

The peak path of provincial carbon emissions in the Yellow River Basin of China Based on scenario analysis and Monte Carlo simulation method

<p>The Yellow River Basin is an important economic zone in China and an important base for energy, chemical, raw materials and basic industries. However, the sloppy development pattern of "high consumption and high pollution" in nine provinces along the Yellow River Basin has become an important obstacle to achieving the carbon peaking and carbon neutrality goals. Using the extended STIRPAT model and combining scenario analysis and Monte Carlo simulation to build a prediction model, the future trend of carbon emissions in the nine provinces of the Yellow River Basin was predicted and the optimal path to reach the peak of carbons in the Yellow River Basin was explored. The results show that the nine provincial areas in the Yellow River Basin will not be able to reach the carbon peaking before 2035 under the baseline scenario. By further comparing the carbon peaking conditions of provinces under the low-carbon scenario and the technology breakthrough scenario, it is found that the peaking time, carbon peak value and the number of provinces under the technology breakthrough scenario are significantly better than those under the low-carbon scenario. Under the technology breakthrough scenario, the energy efficiency of the nine provinces in the Yellow River Basin will be greatly improved, the innovation capacity will be significantly enhanced, and the synergistic emission reduction mechanism and related policies between provincial areas will be perfected. Technological breakthroughs will become an important engine for high-quality development in the Yellow River Basin.</p>

Research on Load Modeling Method for Typical Low Carbon Energy Consumption Scenarios in Border and Cross border Regions Considering Seasonal Migration Characteristics

With the process of urbanization and the ‘the Belt and Road’ initiative, the cross-border energy demand in southwest China has grown rapidly, driving the development of the energy system. The accuracy of load forecasting directly affects the application of energy systems, so it is crucial to conduct research on load forecasting for energy terminals in border and cross-border areas. However, there is a seasonal shift in the diverse energy consumption loads in border and cross-border regions, and currently, research on load forecasting and simulation of typical low-carbon energy consumption scenarios under this feature is basically in a blank state. Based on existing problems, this article conducts research on load modeling methods under the significant ‘seasonal migration’ characteristics of border and cross-border loads, conducts research on characteristic industries in border and cross-border areas, establishes typical low-carbon energy consumption scenarios and simulation models in border and cross-border areas, and uses sensitivity analysis method of dynamic simulation to analyze the impact of different influencing factors on the load of various building types, The Monte Carlo simulation prediction method is used to obtain the sensitivity probability distribution of various influencing characteristic factors, and the typical energy consumption building load model is modified. Finally, by comparing the energy consumption simulation results with statistical results, the accuracy of simulation energy consumption prediction is verified to be higher than 90%.

Response of Ecosystem Service Value to Landscape Pattern Changes under Low-Carbon Scenario: A Case Study of Fujian Coastal Areas

Assessing the influence of landscape pattern changes on ecosystem service value (ESV) is critical for developing land-use polies and increasing ecosystem services. The data sources include remote-sensing image data and statistical yearbooks from 2000, 2010, and 2020. This study employs the patch-generating land-use simulation model, landscape pattern index, and ecological service value estimation to analyse the changes in landscape patterns and ESV in Fujian coastal areas over the last 20 years. The landscape pattern and ESV in the future (2050) are then simulated under the low-carbon scenario (LCS), with the natural development scenario (NDS) serving as a comparison. The results show that: (1) the most noticeable changes from 2000 to 2020 are the reduced cultivated land area and the rapid expansion of construction land area. By 2050, construction land will account for 7.67% of the total land area under LCS, whereas NDS will account for 9.45%, and changes in the landscape pattern indices all indicate there will be greater variety and fragmentation of the landscape, with the NDS being more serious than the LCS; (2) From 2000–2020, the total ESV value showed a decreasing trend. In 2050, the ESV under the LCS will be 122.387 billion yuan, which is higher than the 121.434 billion yuan under the NDS. Regulating services contribute the most to the total ESV, followed by support services; and (3) In the past 20 years, except for a slight increase in water area, the ESV of other landscapes has decreased, with a net decrease of 3.134 billion yuan in total. The R2 fitting between the area change of cultivated and construction land and the total ESV reached 0.9898 and 0.9843, respectively. The correlations between ESV and landscape indices indicate that landscape pattern changes significantly impact ESV. Simulating ESV in LCS can provide guidance for optimising landscape patterns, promoting the benign operation of the regional ecosystem, and achieving sustainable ecological development.

Carbon emission scenario forecast and emission reduction path of China's logistics industry from the perspective of green supply chain management

Green logistics is an important part of green supply chain management, and its green and low-carbon level greatly affects the level of green supply chain management. In order to achieve China's targets of carbon peaking and carbon neutrality, and to study the emission reduction path of the logistics industry under green supply chain management, this paper uses the scalable STIRPAT model and ridge regression to analyze the influencing factors of carbon emissions in the logistics industry; Taking the relevant data from 2000 to 2020 as an example, combined with the scenario analysis method, this paper conducts an empirical study on the development trend of carbon emissions in China's logistics industry in the future. The results show that: under the low-carbon and enhanced low-carbon scenarios, China's logistics industry can achieve the carbon peak target of 876.6 MtCO2 and 817.02 MtCO2 respectively. Carbon emissions can be reduced to 234.9 MtCO2 by 2060 in the Enhanced Low Carbon Scenario. There is still a certain distance between neutralization. In the meantime, according to the forecast results, this paper, from the perspective of green supply chain management, from the three directions of carbon trading, reverse logistics and emission reduction costs, gives suggestions on the emission reduction path of China's logistics industry under the carbon peaking and carbon neutrality goals: consider early Incorporate the logistics industry into the carbon trading market; increase support and accelerate the development of reverse logistics; increase scientific research investment in clean energy, and promote new energy vehicles. This paper innovatively stands in the perspective of supply chain management, and improves the analysis of influencing factors. For the single industry of logistics industry, the carbon peaking and carbon neutrality goals goal is put in a model, which can better reflect the complete trend and target Provide low-carbon directions and suggestions. In all, these findings contribute to the formulation and implementation of sound policies to help China achieve the carbon peaking and carbon neutrality goals.

Financial risks to coal value chain from a cost-conscious shift to renewables in India

A realignment of the financial sector is necessary to both enable the energy system transformation and manage financial risks implied by a transition to net-zero emissions. These include transition risks stemming from policies that limit or price greenhouse gas emissions. The financial sector has turned to scenarios developed by the research community for information on how transitions may unfold. Emerging methodologies linking transition scenarios to risk assessment are in their early stages but are key to enable financial institutions (FIs) to carry out the task at hand. Commercial FIs are exposed to transition risks primarily through their portfolio holdings and how assets therein may fare in a transition. Understanding this counterparty risk is key for development and interpretation of climate-financial scenarios. FIs will need to consider how the firms in a portfolio—the counterparties—will react to the transition and their capacity to navigate the changes involved. Here we apply a transparent and flexible framework to explore transition risks to corporate firms from low-carbon transition scenarios. We show that considering firms’ strategic responses to the changes in their operating environment is an important determinant of the resulting transition risk estimates. We provide an illustrative case study of the coal value chain in India to demonstrate how the framework can be applied to both risk assessment and business strategy setting.

Drivers and effects of digitalization on energy demand in low-carbon scenarios

ABSTRACT The world is currently facing two socio-technical transitions: shifting to a low-carbon society, and a digital revolution. Despite some claims to the contrary, evidence suggests that spread and adoption of information and communication technology (ICT) does not automatically lead to reduction in energy demand, if this stimulates new energy-using practices or wider economic growth. Despite this policy challenge, the two transitions are often considered separately. This study examines potential drivers of reductions or increases in energy demand due to digitalization identified in recent leading global and UK net-zero transitions scenarios. We analyse the scenarios in terms of effects of digitalization on energy demand by identifying specific direct effects of ICT; indirect and rebound effects in transport and home energy use; and wider effects via economic growth. This analysis implies that the future pathways adopted for digitalization will have a significant impact on future energy demand and hence on the feasibility and acceptability of achieving net-zero goals. We find there are different assumptions and development pathways between scenarios. We also identify a need for better inclusion of behavioural effects and other social science understanding in scenarios on the one hand, and recognition that policy can affect digitalization pathways on the other. Overall, our work suggests opportunities for further research and potential for improving policy interactions between these two transitions, and stimulating greater public debate on the different framings for an ICT-driven low-carbon transition. Key policy insights Modelling and scenario development of low-carbon pathways need to pay more attention to drivers of energy demand, including direct and indirect effects of digitalization. Social science understanding of the behavioural change effects of digitalization needs to be considered in assessing energy demand changes. Different pathways of digitalization result from individual and collective choices, including technology development, business models and data protection. Policy makers should seek to promote pathways that deliver social wellbeing and local environmental benefits, not just economic gains.

Monitoring Shoreline and Land Use/Land Cover Changes in Sandbanks Provincial Park Using Remote Sensing and Climate Data

Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters such as land-use/land-cover (LULC) change should be considered. This paper presents a remotely sensed shoreline monitoring in Sandbanks Provincial Park, Ontario, Canada, from 1984 to 2021. The CoastSat toolkit for Python and a multilayer perceptron (MLP) neural network classifier were used for shoreline detection, and an unsupervised change detection framework followed by a postclassification change detection method was implemented for LULC classification and change detection. The study assessed the recent coastal erosion and accretion trends in the region in association with spatiotemporal changes in the total area of the West and East Lakes, the transition between LULC classes, extreme climate events, population growth, and future climate projection scenarios. The results of the study illustrate that the accretion trend apparently can be seen in most parts of the study area since 1984 and is affected by several factors, including lake water-level changes, total annual precipitations, sand movements, and other hydrologic/climatic parameters. Furthermore, the observed LULC changes could be in line with climate change-driven forces and population growth to accelerate the detected accretion trend in the East and West Lakes. In total, the synergistic interaction of the investigated parameters would result in a greater accretion trend along with a lower groundwater table amid even a low carbon scenario. The discussed findings could be beneficial to regional/provincial authorities, policymakers, and environmental advocates for the sustainable development of coastal communities.

Spatiotemporal variation of land-use carbon emissions and its implications for low carbon and ecological civilization strategies: Evidence from Xiamen-Zhangzhou-Quanzhou metropolitan circle, China

• LUCE increased by 1284.78×10 4 tons from 2000 to 2020. • LCES scenario decreased LUCE with 128.74 × 10 4 tons than that in BAU scenario. • Area and management of arable land, forest land and construction land should be regulated. • The policy effectiveness and adaptation to local conditions should be ensured. This study investigated the spatiotemporal variation of historical and future land-use carbon emission (LUCE) in the Xiamen-Zhangzhou-Quanzhou metropolitan circle, China, to obtain policy implications for low carbon and ecological security by simulating land use change under low carbon and ecological security (LCES) scenario and business as usual (BAU) scenario in 2030. Our results showed that: (1) Under LCES scenario, the area of arable land and construction land was 1.17% and 0.40% less than that under BAU scenario, whereas the area of forest land was 1.90% more than that. (2) LUCE increased by 1284.78×10 4 tons totally in 2000–2020. Whereas under LCES scenario, LUCE was 128.74×10 4 tons less than that under BAU scenario, and the carbon absorption of forest land increased by 24.90×10 4 tons. (3) At city scale, LUCE in cities followed the order of Quanzhou>Zhangzhou>Xiamen in 2000–2030. The county scale witnessed rapid-growth and heavy LUCE in coast over time. LUCE at grid scale showed an expansion from coast to inland all the time, but it was alleviated under LCES scenario. The quantity and spatial structure of land use types should be optimized, and low-carbon and ecological classification management should be implemented at arable land, forest land and construction land strictly.