• Energy Research

The integration and optimization of protected areas is an important part of the construction of a protected areas system centered around national parks. How to best integrate and optimize protected areas is the most urgent problem in the reform. This paper analyzes the spatial overlap and continuity of protected areas in China on a national scale and proposes a collection of candidate areas for national parks. The results show that ➀ 52.9% of the protected areas overlap, with nature reserves, forest parks and scenic areas showing the most overlap, and the maximum number of overlaps is five. ➁ There are 1145 groups of contiguous protected areas that form continuous boundary areas, accounting for 58.9% of the total number of protected areas analyzed in this paper. Of these continuous zones, 48.55% consists of only two protected areas. There are 51 continuous areas with more than 10 protected areas, showing point continuous and/or patchy continuous boundary characteristics. ➂ According to the identified continuous areas, overlapping degree, protection levels and function, the candidate areas of national parks in China are proposed. Continuous areas with comprehensive ecosystem services, high-intensity protection levels, and high overlap intensity are selected as preliminary candidate areas for national parks. These are further refined based on their co-location with four types of key areas. A total of 41 areas are recommended as potential national parks. These continuous areas are highly consistent with the national “two screens, three belts” strategy, and nine of them are essentially consistent with the current national parks pilot. These results indicate that the recommended areas selected according to this research method are reasonable, and can provide a scientific basis for determining the spatial layout of China’s new protected areas system and the establishment of national parks.

An increasing number of nature reserves are being invaded by various development and construction activities, such as energy, resources, and transportation facilities. The ecological footprint model, which enables a quantitative assessment of ecological sustainability, can assess whether human consumption at various spatial scales falls within the regenerative capacity of the biosphere. Based on the traditional ecological footprint evaluation model: the Global Agro-Ecological Zone (EF-GAEZ model), this study proposes an improved ecological footprint model based on net primary productivity (EF-NPP model) and its validations. In this study, the status of ecological footprints and the ecological carrying capacities of 319 national nature reserves in 2010 is explored, and the changes in ecological surpluses and ecological deficits from 2000 to 2010 are analyzed. The ecological footprint per capita and the ecological carrying capacity per capita calculated by the two models were mostly consistently at the same level (more than 68%), which indicated that the ecological footprint per capita and the ecological carrying capacity per capita of the two models followed the same rule. The EF-NPP model can reflect the change in the global climate, the degradation of the soil, and the progress of the technology.

Maximizing the development of renewable energy plays a critical role in mitigating the climate crisis. Marginal land provides space for the development of biomass energy; however, it remains unclear how the amount and spatial distribution of marginal land that is suitable for energy crop development will change in the future. Here, we project energy marginal land changes in China following the shared socioeconomic pathway (SSP) and/or representative concentration path (RCP). We provide datasets of marginal land, agriculturally suitable land, and potentially suitable for energy crops under historical scenarios and six future scenarios (i.e. SSP1–1.9, SSP1–2.6, SSP4–3.4, SSP2–4.5, SSP4–6.0, and SSP3–7.0) for the period 2020–2100, with a spatial resolution of 5 km. Under the six scenarios, from 2020–2100, the area of suitable marginal land ranged from 1.90–16.28 (Jatropha curcas L.) to 37.37–73.97 (Panicum virgatum L.) (×104 km2), depending on the choice of energy crops and climate scenario. Based on the growing suitability of eight important bioenergy crops—Ricinus communis L., Saccharum officinarum L., Pistacia chinensis Bunge, Panicum virgatum L., Jatropha curcas L., Miscanthus giganteus J., Manihot esculenta Crantz, and Sorghum bicolor Moench—our dataset can be used to identify suitable locations for specific energy crops. This new synthetic dataset could support the development of multiscenario-based solutions related to carbon neutrality, ecosystem services, and energy transition.

AbstractUnderstanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000–2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.

With the increasing amount of digital signage and the complexity of digital signage services, the problem of introducing precise location recommendation methods for digital signage should be solved by digital signage enterprises. This research aims to provide a sustainable location recommendation model that integrates the spatial characteristics of geographic locations and multi-source feature data to recommend locations for digital signage. We used the outdoor commercial digital signage within the Sixth Ring Road area in Beijing as an example and combined it with economic census, population census, average house prices, social network check-in data, and the centrality of traffic networks that have an impact on the sustainable development of the regional economy as research data. The result shows that the proposed method has higher precision and recall in location recommendation, which indicates that this method has a better recommendation effect. It can further improve the recommendation quality and the deployment of digital signage. By this method, we can optimize resource allocation and make the economics and resources sustainable. The digital signage recommendation results of the Beijing City Sixth Ring Road indicated that the areas suitable for digital signage were primarily distributed in Wangfujing, Financial Street, Beijing West Railway Station, and tourist attractions in the northwest direction of the Fifth Ring Road. The research of this paper not only provides a reference for the integration of geographical features and their related elements data in a location recommendation algorithm but also effectively improves the science of digital signage layout, prompting advertising efforts to advance precision, personalization, low carbonization, and sustainable development.

Résumé Objectifs Comprendre si et comment la pandémie de COVID-19 affecte le risque de différents types de conflits dans le monde dans le contexte du changement climatique. Méthodologie Sur la base de la base de données des conflits armés, de la COVID-19, des données climatiques détaillées et des données non climatiques couvrant la période 2020–2021, nous avons appliqué la modélisation des équations structurelles spécifiquement pour réorganiser les liens entre le climat, la COVID-19 et le risque de conflit. De plus, nous avons utilisé la méthode de l'arbre de régression boosté pour simuler le risque de conflit sous l'influence de multiples facteurs. Résultats Le risque de transmission de la COVID-19 semble diminuer à mesure que la température augmente. En outre, la COVID-19 a un impact mondial important sur le risque de conflit, bien qu'il existe des variations régionales et de risque de conflit. De plus, lorsque nous testons un effet différé d'un mois, nous constatons une cohérence entre les régions, indiquant une influence positive de la COVID-19 sur les manifestations (manifestations et émeutes) et une relation négative avec le risque de conflit non étatique et violent. Conclusion La COVID-19 a un effet complexe sur le risque de conflit dans le monde entier dans le contexte du changement climatique. Implications Jeter les bases théoriques de la façon dont la COVID-19 affecte le risque de conflit et fournir une certaine inspiration pour la mise en œuvre des politiques pertinentes. Resumen Objetivos Comprender si y cómo la pandemia de COVID-19 afecta el riesgo de diferentes tipos de conflictos en todo el mundo en el contexto del cambio climático. Metodología Con base en la base de datos de conflictos armados, COVID-19, clima detallado y datos no climáticos que cubren el período 2020–2021, aplicamos el Modelado de Ecuaciones Estructurales específicamente para reorganizar los vínculos entre el clima, COVID-19 y el riesgo de conflicto. Además, utilizamos el método del Árbol de Regresión Impulsado para simular el riesgo de conflicto bajo la influencia de múltiples factores. Hallazgos El riesgo de transmisión de COVID-19 parece disminuir a medida que aumenta la temperatura. Además, la COVID-19 tiene un impacto mundial sustancial en el riesgo de conflicto, aunque existen variaciones regionales y de riesgo de conflicto. Además, al probar un efecto rezagado de un mes, encontramos coherencia entre las regiones, lo que indica una influencia positiva de COVID-19 en las manifestaciones (protestas y disturbios) y una relación negativa con el riesgo de conflictos no estatales y violentos. Conclusión El COVID-19 tiene un efecto complejo sobre el riesgo de conflicto en todo el mundo bajo el cambio climático. Implicaciones Sentar las bases teóricas de cómo la COVID-19 afecta el riesgo de conflicto y proporcionar algo de inspiración para la implementación de políticas relevantes. Abstract Objectives Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020–2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies. الملخص الأهداف فهم ما إذا كانت جائحة كوفيد-19 تؤثر على مخاطر أنواع مختلفة من النزاعات في جميع أنحاء العالم في سياق تغير المناخ وكيفية تأثيرها. المنهجية استنادًا إلى قاعدة بيانات النزاع المسلح وكوفيد-19 والمناخ التفصيلي والبيانات غير المناخية التي تغطي الفترة 2020–2021، طبقنا نمذجة المعادلة الهيكلية على وجه التحديد لإعادة تنظيم الروابط بين المناخ وكوفيد-19 ومخاطر النزاع. علاوة على ذلك، استخدمنا طريقة شجرة الانحدار المعزز لمحاكاة مخاطر النزاع تحت تأثير عوامل متعددة. النتائج يبدو أن خطر انتقال COVID -19 ينخفض مع ارتفاع درجة الحرارة. بالإضافة إلى ذلك، فإن COVID -19 له تأثير كبير في جميع أنحاء العالم على مخاطر النزاع، على الرغم من وجود اختلافات إقليمية ومخاطر النزاع. علاوة على ذلك، عند اختبار تأثير متأخر لمدة شهر واحد، نجد الاتساق عبر المناطق، مما يشير إلى التأثير الإيجابي لـ COVID -19 على المظاهرات (الاحتجاجات وأعمال الشغب) والعلاقة السلبية مع مخاطر النزاع العنيف وغير الحكومي. الخاتمة كوفيد-19 له تأثير معقد على مخاطر النزاع في جميع أنحاء العالم في ظل تغير المناخ. الآثار وضع الأساس النظري لكيفية تأثير كوفيد-19 على مخاطر النزاع وتوفير بعض الإلهام لتنفيذ السياسات ذات الصلة.

AbstractThis article identifies marginal land technically available for the production of energy crops in China, compares three models of yield prediction forMiscanthus × giganteus,Panicum virgatumL.(switchgrass), andJatropha, and estimates their spatially specific yields and technical potential for 2017. Geographic Information System (GIS) analysis of land use maps estimated that 185 Mha of marginal land was technically available for energy crops in China without using areas currently used for food production. Modeled yields were projected forMiscanthus × giganteus, a GIS‐based Environmental Policy Integrated Climate model for switchgrass and Global Agro‐Ecological Zone model forJatropha. GIS analysis and MiscanFor estimated more than 120 Mha marginal land was technically available forMiscanthuswith a total potential of 1,761 dry weight metric million tonne (DW Mt)/year. A total of 284 DW Mt/year of switchgrass could be obtained from 30 Mha marginal land, with an average yield of 9.5 DW t ha−1 year−1. More than 35 Mha marginal land was technically available forJatropha, delivering 9.7 Mt/year ofJatrophaseed. The total technical potential from available marginal land was calculated as 31.7 EJ/year forMiscanthus, 5.1 EJ/year for switchgrass, and 0.13 EJ/year forJatropha. A total technical bioenergy potential of 34.4 EJ/year was calculated by identifying best suited crop for each 1 km2grid cell based on the highest energy value among the three crops. The results indicate that the technical potential per hectare ofJatrophais unable to compete with that of the other two crops in each grid cell. This modeling study provides planners with spatial overviews that demonstrate the potential of these crops and where biomass production could be potentially distributed in China which needs field trials to test model assumptions and build experience necessary to translate into practicality.