Comprehensive Scenario Research Articles

Assessing sustainability of microalgae-based wastewater treatment: Environmental considerations and impacts on human health

An integrated life cycle assessment (LCA) and quantitative microbial risk assessment (QMRA) were conducted to assess microalgae-mediated wastewater disinfection (M-WWD). M-WWD was achieved by replacing ultraviolet disinfection with a microalgal open raceway pond in an existing sewage treatment plant (STP) in India. Regarding impacts on human health, both M-WWD and STP yielded comparable life cycle impacts, around 0.01 disability-adjusted life years (DALYs) per person per year. However, QMRA impacts for M-WWD (0.053 DALYs per person per year) were slightly lower than that for STP while considering exposure to E. coli O157:H7 and adenovirus. Additionally, a comparative LCA resolved the dilemma about the appropriate utilization of microalgal biomass. Among biodiesel, biocrude, and biogas production, the lowest impacts of 0.015 DALYs per person per year were obtained for biocrude for 1 m3 water treated by M-WWD. Electricity consumption in microalgae cultivation was a major environmental hotspot. Overall, M-WWD, followed by production of microalgal biocrude, emerged as a sustainable alternative from environmental and public health perspectives. These findings set the foundation for pilot-scale M-WWD system development, testing, and economic evaluation. Such comprehensive investigations, encompassing LCA, QMRA, and resource recovery scenarios, offer crucial insights for stakeholders and decision-makers in wastewater treatment and environmental management.

How Would Structural Change in Electricity and Hydrogen End Use Impact Low-Carbon Transition of an Energy System? A Case Study of China

Driven by global targets to reduce greenhouse gas emissions, energy systems are expected to undergo fundamental changes. In light of carbon neutrality policies, China is expected to significantly increase the proportion of hydrogen and electricity in its energy system in the future. Nevertheless, the future trajectory remains shrouded in uncertainty. To explore the potential ramifications of varying growth scenarios pertaining to hydrogen and electricity on the energy landscape, this study employs a meticulously designed bottom-up model. Through comprehensive scenario calculations, the research aims to unravel the implications of such expansions and provide a nuanced analysis of their effects on the energy system. Results show that with an increase in electrification rates, cumulative carbon dioxide emissions over a certain planning horizon could be reduced, at the price of increased unit reduction costs. By increasing the share of end-use electricity and hydrogen from 71% to 80% in 2060, the unit carbon reduction cost will rise by 17%. Increasing shares of hydrogen could shorten the carbon emission peak time by approximately five years, but it also brings an increase in peak shaving demand.

A Fast Optimal Coordination Method for Multiagent in Complex Environment

Facing the implementation problems such like low growth reward, long training time, and poor stability of the multiagent learning methods when dealing with complex environment and more agents, this paper proposes a fast optimal coordination method for multiagent in complex environment (FOC-MACE). Firstly, the environment exploration strategy is introduced into the policy network based on the MADDPG method for higher growth rewards. Then, the parallel computing technology is adopted in the critic network, in purpose to effectively reduce the training time. These tactics together are beneficial to enhance the stability of multiagent learning. Lastly, the optimal resource allocation is carried out to realize optimal coevolution of the multiagents and further improve the learning ability of the agents’ group. To verify the effectiveness of our proposal, the FOC-MACE is compared with several advanced methods at current stage in the MPE environment. Three different experiments prove that by using our method, the growth reward is increased by up to 37.1%, the training is speed up significantly, and the stability of the method, which represented by standardized variance, is also improved. In addition, this paper validated the fast optimal coordination method for multiagent systems in the context of UAV scenarios, demonstrating the practical performance of the approach. Through comprehensive experiments and scenario validations, the study successfully confirmed the effectiveness of the proposed fast optimal coordination method for multiagent systems in complex environments.

Breast cancer risk SNPs and associated expression QTLs focusing Bangladeshi population: An in silico analysis

BackgroundSingle Nucleotide Polymorphism (SNP)s in the genome and associated genes cause susceptibility to breast cancer, the most common cancer leading to death in women. Variation in different human races' genomes makes breast cancer prognosis challenging in terms of targeted drugs and therapies. The study aimed to compile the Breast cancer associated SNPs used for screening in the existing publications focusing Bangladeshi population, followed by the identification of Expression quantitative trait loci (eQTLs) associated with those SNPs from the eQTL database. eQTLs identify genes whose expression is regulated by specific SNPs. In silico characterization in terms of variant effect prediction, co-expression, Gene Ontology (GO) enrichment, protein-protein interaction, and sequence motif analysis narrowed down a specific set of candidate genes. MethodsPublished reports emphasizing the SNPs screened for Breast cancer in Bangladeshi population were analyzed in PancanQTL for identification of eQTLs which uses genotype and gene expression data from The Cancer Genome Atlas. The gene description and GO associated with identified eQTLs were retrieved from the Ensembl database and characterizations were performed using variant effect predictor, Coexpedia, MEME suite, and STRINGdb. ResultsIt was found from the published reports that not all variants showed strong association with the disease in Bangladeshi population. The cis-eQTLs associated with reported SNPs tested so far on Bangladeshi population are ZNF575, MRPL42P5, C15orf57, C15orf62, NFATC3, XRCC1, C14orf153, CKB, BAG5, KLC1, MARK3. Among them only ZNF575 was enlisted as breast cancer associated eQTL and the rest are linked with other types of cancer. These genes are mostly associated in DNA-binding transcription factor activity, protein binding, Intracellular protein transport II, transferase activity. Protein-protein interaction could predict some functional partners to connect the eQTLs with respective SNP harboring genes. Taking the commonly screened genes for breast cancer as targets breast cancer associated cis and trans eQTLs along with the associated survival eQTLs have been retrieved from the database and a list of specific variants are recommended for future studies to get a more comprehensive scenario about the disease prognosis. ConclusionSince it was found from the existing literature that the commonly used variants are not always associated with all human races, this simple and precise in silico study was carried out to analyze publicly available data. This helped limit specific candidate genes and variants which will be helpful in future population-based screenings in understanding breast cancer prognosis with a provision to develop population specific personalized drug.

Simulating the interrelationships among population, water, ecology, and economy in urban agglomerations based on a system dynamics approach

China's urban agglomerations (UAs) gather most of China's population and economy, but they also facing serious water resources crisis and ecological risks. How to manage the interrelationships among the population, water, ecology, and economy (PWEE) in UAs is crucial for the future of China's urbanization development. Based on the system dynamics (SD) approach, we constructed an SD model for simulating the interrelationships among PWEE in UAs, elucidating the intricate nonlinear relationships between elements of the PWEE system through causal feedback processes and model equations. In addition, we take the Tianshan North slope urban agglomeration (TSNSUA) as an example, which is a representative area where water resources crisis and ecological risk coexist, and designed five scenarios to dynamically simulate the balance of water supply and demand under the high-density population and economic agglomeration, as well as the potential ecological environmental effects in the process of urban spatial expansion. Our findings include: (1) The existing water resources conditions of TSNSUA can continue to support the current population and economic growth rates. If the future development plan follows the status quo continuation scenario, the phenomenon of insufficient water supply will not occur on the premise that the water supply capacity remains at its current highest level. (2) TSNSUA's future urban spatial expansion is predicted to have less overall impact on the ecological environment, and the carbon peak target by 2030 has an opportunity to be achieved. (3) Appropriately reducing the proportion of agricultural water, while strengthening water-saving and ecological protection measures, can ensure that TSNSUA's population and economy maintain a medium to high growth rate. Based on scenario comparison, our recommendation to policymakers is to establish the development objectives of TSNSUA based on the comprehensive development scenario.

Creating the Dutch One Health Shared Socio-economic Pathways (SSPs)

The world is changing, in terms of both climate and socio-economics. These changes have the potential to have a profound impact on the health of humans, animals and the environment, often grouped together as ‘One Health’. Humans, animals and the environment are closely interlinked and to determine realistic future vulnerabilities we must consider everything together. We need comprehensive scenarios which cover a broad range of variables affecting One Health. We developed a methodology to create national-level One Health scenarios based on the global Shared Socio-economic Pathways (SSPs), which we applied to the Netherlands. We identified variables which should be included in such scenarios and gathered input from existing scenarios, stakeholder consultation and current plans and commitments. This information was combined to create detailed descriptions, which were used to assess the main health risks under each scenario. All the scenarios face similar challenges, for example an aging population, introductions of infectious diseases and rising sea-levels and extreme weather events; however, in some scenarios, they deal with these challenges much better than in others. The healthiest outcome was achieved when a policy of low greenhouse gas emissions was combined with a well-functioning society which looks after both its citizens and the environment. These scenarios can be used to analyse specific health risks and to consider options for mitigation and preparedness. Because they are national-level scenarios, they allow the local context, policies and customs to be accounted for and should be a valuable tool for protecting One Health in the future.

Projecting the response of ecological risk to land use/land cover change in ecologically fragile regions

Anthropogenic activities have dramatically altered land use/land cover (LULC), leading to ecosystem service (ES) degradation and further ecological risks. Ecological risks are particularly serious in ecologically fragile regions because trade-offs between economic development and ecological protection are prominent. Thus, ways in which to assess the response of ecological risks to LULC change under each development scenario in ecologically fragile regions remain challenging. In this study, future LUCC and its impact on ESs under four development scenarios in 2040 in western Jilin Province were predicted using a patch-generating land use simulation model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Ecological risk was assessed based on future LUCC possibilities, and potential ES degradation and potential drivers of ecological risks were explored using a geographic detector. The results showed that the cropland development scenario (CDS) would experience large-scale urbanization and cropland expansion. Carbon storage (CS), habitat quality (HQ), and water purification (WP) degraded the most under the CDS, and grain yield (GY) and water yield (WY) degraded the most under the ecological protection scenario (EPS). The LUCC probability under the CDS (14.37 %) was the highest, while the LUCC probability under the comprehensive development scenario (CPDS) (8.68 %) was the lowest. The risk of WP degradation was greatest under the CDS, but the risk of soil retention (SR) degradation was greatest under the natural development scenario (NDS), EPS, and CPDS. Ecological risk coverage was the largest (98.04 %), and ecological risks were the highest (0.21) under the CDS, while those under the EPS were the opposite. Distance to roads and population density had a higher impact on ecological risks than other drivers. Further attention should be given to the ecological networks and pattern establishment in urbanized regions. This study will contribute to risk prevention and sustainable urban and agricultural development.

Uncovering the lifecycle carbon emissions and its reduction pathways: A case study of petroleum refining enterprise

The petroleum refining industry is considered energy-intensive and responsible for significant carbon emissions. This study applied both life cycle assessment and system dynamics to comprehensively analyze and simulate the factors that influence corporate carbon emissions. This analysis elucidates the complex interplay of mechanisms that can reduce corporate carbon emissions. This study also provides a deeper understanding of how these mechanisms can reduce emissions over time. The results show that the fuel combustion phase has the highest carbon intensity (0.25 tons CO2e/ton), followed by the industrial production phase (0.09 tons CO2e/ton) and the torch combustion phase (3.31E-04 tons CO2e/ton) throughout the life cycle of the sample enterprise. Notably, carbon emissions from the net purchased thermal energy are “negative,” meaning that steam thermal production from industrial processes may provide environmental benefits through the carbon cycle. Multi-scenario analysis shows that adjusting a single parameter will not achieve the “carbon neutrality” emissions reduction target for the sample enterprise. Instead, adjusting the energy structure and optimizing efficiency represents the first steps toward such a target. According to the comprehensive scenario, the sample enterprise will reach peak carbon emissions by 2025 and eventually achieve “carbon neutrality” by 2058.

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving.

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models' tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

A minimal kinetic model for the interpretation of complex catalysis in single enzyme molecules.

Multi-exponential waiting-time distribution and randomness parameter greater than unity ascribe dynamic disorder in single-enzyme catalysis corroborated to the interplay of transforming conformers [English et al., Nat. Chem. Biol., 2006, 2, 87]. The associated multi-state model of enzymatic turnovers with statically heterogeneous catalytic rates misdescribes the non-linear uprising of the randomness parameter from unity in relation to the attributes of the fall-offs of the waiting-time distribution at different substrate concentrations. To resolve this crucial issue, we first employ a comprehensive stochastic reaction scenario and further rationalize and work out the minimal indispensable dynamic-disorder model that ensures the foregoing relationship upon comparison with the data. We elucidate that specific disregard for the transition rate coefficients in the multi-state model on account of the especially slow conformational transitions is the underlying reason for not achieving interrelation between the observables.

A review of existing model-based scenarios achieving SDGs: progress and challenges

Abstract Non-Technical Summary In 2015, the United Nations articulated the ambition to move toward a prosperous, socially inclusive, and environmentally sustainable future for all by adopting the Sustainable Development Goals (SDGs). However, little is known about the pathways that could lead to their concurrent achievement. We provide an overview of the current literature on quantitative pathways toward the SDGs, indicate the commonly used methods and indicators, and identify the most comprehensive pathways that have been published to date. Our results indicate that there is a need for more scenarios toward the full set of SDGs, using a wider range of underlying narratives. Technical Summary Quantitative goal-seeking scenario studies could help to explore the needed systems' transformations to implement the 2030 Agenda for Sustainable Development by identifying enabling conditions and accounting for the synergies and trade-offs between the SDGs. Given that the SDGs were adopted some time ago, here, we review the existing global scenario literature to determine what it can offer in this context. We found only a few scenarios that address a large set of SDGs, while many more deal with specific clusters of 2–6 SDGs. We identified the most frequent clusters and compared the results of the most comprehensive sustainable development scenarios. The latter is complicated because of the diversity of methods, indicators, and assumptions used. Therefore, we suggest that an effort is needed to develop a wider set of scenarios that would achieve multiple SDGs, using a more standardized framework of targets and indicators. Social Media Summary This study reviews the current global pathways toward the SDGs and shows the need for a broader set of SDG scenarios.

Extension of an agent-based simulation for the optimized allocation of freight requests to differently structured supply chains

The paper deals with the extension of the logistics simulation within an agent-based simulation framework to be able to consider the delivery across several supply chains. The logistics simulation is placed in the context of the underlying freight transportation simulation. The use of multiple possible logistics chains addresses a key limitation of previous studies. Logistics chains represent the various transportation options of a Logistic Service Provider (LSP) and are made up of resources such as depots, logistics hubs and carriers that the logistics service provider can commission. When implementing the new functionality, the existing flow of the simulation process must be considered. In particular, changes need to be made to the assignment of freight requests to logistics chains, the iterative optimization of this assignment and scheduling. Appropriate assignment methods and rescheduling strategies are developed and presented. These innovations reveal compatibility problems within the existing simulation framework. The functionality developed is presented in constructed scenarios, highlighting the advantages of using multiple logistics chains. In addition, the applicability is demonstrated in a comprehensive real scenario that includes the delivery of supermarkets.

Research on reinforcement learning based on PPO algorithm for human-machine intervention in autonomous driving

<abstract> <p>Given the current limitations in intelligence and processing capabilities, machine learning systems are yet unable to fully tackle diverse scenarios, thereby restricting their potential to completely substitute for human roles in practical applications. Recognizing the robustness and adaptability demonstrated by human drivers in complex environments, autonomous driving training has incorporated driving intervention mechanisms. By integrating these interventions into Proximal Policy Optimization (PPO) algorithms, it becomes possible for drivers to intervene and rectify vehicles' irrational behaviors when necessary, during the training process, thereby significantly accelerating the enhancement of model performance. A human-centric experiential replay mechanism has been developed to increase the efficiency of utilizing driving intervention data. To evaluate the impact of driving intervention on the performance of intelligent agents, experiments were conducted across four distinct intervention frequencies within scenarios involving lane changes and navigation through congested roads. The results demonstrate that the bespoke intervention mechanism markedly improves the model's performance in the initial stages of training, enabling it to overcome local optima through timely driving interventions. Although an increase in intervention frequency typically results in improved model performance, an excessively high intervention rate can detrimentally affect the model's efficiency. To assess the practical applicability of the algorithm, a comprehensive testing scenario that includes lane changes, traffic signals, and congested road sections was devised. The performance of the trained model was evaluated under various traffic conditions. The outcomes reveal that the model can adapt to different traffic flows, successfully and safely navigate the testing segment, and maintain speeds close to the target. These findings highlight the model's robustness and its potential for real-world application, emphasizing the critical role of human intervention in enhancing the safety and reliability of autonomous driving systems.</p> </abstract>

Analysis of User Acceptance in Fire Extinguisher Simulation Applications with Virtual Reality Technology

This article discusses the use of Virtual Reality (VR) applications to provide comprehensive training scenarios for emergency services such as fire extinguishers. This study presents an initial prototype with 3 levels of variations of fire models that help extinguish virtual fire as a VR training scenario. The Fire Extinguishing VR Application that has been developed is based on literacy technical provisions for fire management in urban areas by the State Minister for Public Works. The prototype provides a VR experience to application users based on the ease of use and learning of fire fighting through questionnaires. The results confirm the value of using VR for training procedures that are difficult to implement in the real world can be realized with 3 levels of fire simulation. A score with a percentage of 92% stated that the application was easy to recognize and use and a score of 89% stated that the application could be used to learn how to extinguish every variation of the fire level scenario that occurred.

Comprehensive Scenario Analyses for Coal exit and Renewable Energy Development Planning of Kazakhstan using PyPSA-KZ

Comprehensive Scenario Analyses for Coal exit and Renewable Energy Development Planning of Kazakhstan using PyPSA-KZ

Advancing coupling coordination simulation in the social-human-ecological system of the Three Gorges Reservoir Area: A multi-scenario system dynamics approach

Due to the lack of scientific and systematic thinking in the early stages of high-quality urbanisation, the interaction and stress between the social-human-ecological (SHE)11SHE social-human-ecological. system intensified, resulting in a series of problems related to the low level of ecological environment and the imbalanced socio-economic development. It is crucial to enhance the safety, health, and well-being of city dwellers, as well as to promote the sustainable development of the region. Based on complex systems science, using the Three Gorges Reservoir Area (TGRA)22TGRA Three Gorges Reservoir Area. as an instance, this study develops a multidimensional coupling conceptual model of the SHE system, employs the system dynamics (SD)33SD system dynamics. method to clarify the SHE system’s causal feedback mechanism, and simulates the coupling coordination degree (CCD)44CCD coupling coordination degree. from 2010 to 2030. Simultaneously, eight scenarios are created to improve the system’s CCD by modifying factors. According to historical and sensitivity test findings, the SD model used in this work can effectively represent the SHE system’s dynamic coupling and interaction connection. Furthermore, under the comprehensive development scenario that takes into account the protection of the ecological environment, human intelligent regulation, and socio-economic development, the TGRA has the biggest growth in CCD. As a result, raising the policy intensity of a single subsystem is insufficient. Balancing policy implementation for each subsystem is advantageous to improving the coupling coordination of the SHE system in the TGRA. The study is of significant importance for achieving high-quality socio-economic development and sustainable ecological management in the TGRA region.

A comprehensive evolutionary scenario for the origin and neofunctionalization of the Drosophila speciation gene Odysseus (OdsH).

Odysseus (OdsH) was the first speciation gene described in Drosophila related to hybrid sterility in offspring of mating between Drosophila mauritiana and Drosophila simulans. Its origin is attributed to the duplication of the gene unc-4 in the subgenus Sophophora. By using a much larger sample of Drosophilidae species, we showed that contrary to what has been previously proposed, OdsH origin occurred 62 MYA. Evolutionary rates, expression, and transcription factor-binding sites of OdsH evidence that it may have rapidly experienced neofunctionalization in male sexual functions. Furthermore, the analysis of the OdsH peptide allowed the identification of mutations of D. mauritiana that could result in incompatibility in hybrids. In order to find if OdsH could be related to hybrid sterility, beyond Sophophora, we explored the expression of OdsH in Drosophila arizonae and Drosophila mojavensis, a pair of sister species with incomplete reproductive isolation. Our data indicated that OdsH expression is not atypical in their male-sterile hybrids. In conclusion, we have proposed that the origin of OdsH occurred earlier than previously proposed, followed by neofunctionalization. Our results also suggested that its role as a speciation gene might be restricted to D. mauritiana and D. simulans.

Generation Expansion Planning considering environmental impact and sustainable development for an Indian state using the LEAP platform

GEP (Generation Expansion Planning) is one of the most complex tasks in which planning is done to find the best possible distinct solutions to install new generating units to meet future load demands. This paper uses the Low Emission Analysis Platform (LEAP) software environment to analyse the GEP for the AP Power sector from 2021 to 2050. In recent years, the AP power sector has faced several issues, such as frequent service cuts due to power shortages. In this paper, five scenarios such as Business As Usual (BAU), Low Demand Growth (LDG), High Demand Growth (HDG), Least Cost Optimization (LCO) and Climate Mitigation Scenarios, have been analysed with environmental, technical, and economic parameters. This study has estimated the total environmental emissions and total cost of all scenarios. At present, Andhra Pradesh has a total installed capacity of 25.4 GW. The best scenarios can be selected based on environmental impact, cost-effectiveness, efficiency, and reliability. The results show that the LDG scenario will be an optimal choice for AP due to the state's economic crisis, and the government should also focus on energy conservation and limit demand growth to 4.5%. While presenting a general GEP, the study presents comprehensive system performance scenarios from which planners can choose.

Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.

Nature's contributions to people responding to landscape stability in a typical karst region, southwest China

Understanding the response of nature's contributions to people (NCP) to change in landscape stability may constrain and guide human modification of nature. However, the effects of landscape stability on NCP, in the context of active human modification, remain unclear. To address this issue, we chose the Wujiang River Basin in the Yangtze River Economic Belt of China as a study area and quantified landscape stability in 2000, 2010, and 2020 under different scenarios. Environmental protection and comprehensive development scenarios were simulated for 2030 using the patch-generating land use simulation (PLUS) model. For current and future scenarios, NCP were assessed in terms of indicators, which included habitat creation and maintenance (NCP1), regulation of freshwater (NCP6), and protection of soils (NCP8). Under the current status quo, urban expansion was 491.547 km2, and landscape stability showed a decreasing trend, with the mean value decreasing from 0.049 in 2000 to 0.015 in 2020. In future scenarios, landscape stability was highest under the comprehensive development scenario. In addition, in terms of changes in NCP, the high-value contributions declined by 16%–48% from 2000 to 2020. Our results clarified how NCP respond to landscape stability. These results can help conservation managers develop more targeted ecological strategies that will constrain human modification activities to enhance well-being.