Economic Scenarios Research Articles

A globalized symbolic environment ward by the States and dominated by organizations: enticing the nobility and influencing the class habitus

Purpose Through Bourdieusian sociology, this study aims to interpret a globalized symbolic environment ward by the States and dominated by organizations through the States’ Nobilities enticing and the Euro-American influences disseminated by the cultural circuit of capitalism in the inculcation and incorporation of a class habitus conniving with this logic of domination. Design/methodology/approach This study has developed a theoretical essay based on the contributions of Bourdieusian sociology to discuss and understand the following concepts and their respective relationships: symbolic environment, globalization, organizations, State, State Nobility, Euro-American influences, cultural circuit of capitalism and class habitus. Findings The arguments built throughout this theoretical essay recognized how class habitus on environment contributes to organizations establishing themselves as a space that consolidates and replicates the domination logic. As indicated, the State Nobility is an intermediary element between dominant organizations and the State, as dominated. Practical implications This theoretical essay signals that less harmful alliances between organizations, the State Nobility and the State could culminate in social, environmental and economic scenarios provided with more inclusion, diversity and preservation. Social implications This study presents an in-depth conceptual analysis to hold power structures responsible as direct and indirect drivers of environmental problems, with their different proportions and severity levels, affecting the planet. Originality/value This study proposes an alternative lens to debate and question how much the results presented by the contemporary world order compensate (if in any way) the damage that invades and deteriorate environmental assets.

An Analysis on Determinants of Cash and L/C Export: Findings from Türkiye’s Export

The payment methods used in international trade are of significant importance for the financial flow of the involved parties. When it comes to transferring goods and funds across borders to their new owners, the risks faced by the parties may seem daunting. The use of a letter of credit payment method by banks serves a protective role, enabling the parties to engage in trade more securely. While the expenses linked to this approach are sometimes viewed as challenging for the involved parties, it is considered a significant enabler, particularly in new trade alliances. In fact, exporters' choices are classified in countries' trade statistics based on payment methods. This study aims to uncover the determinants of exports from Türkiye between January 2013 and September 2023, utilizing both cash in advance and letter of credit payment methods. To accomplish this objective, exchange rates and specific inflation indicators, and their causal relationship with export transactions based on payment types was examined using the Toda-Yamamoto causality test. The research findings indicate that exchange rates, inflation, and cash payment variables cause letter of credit payments. Conversely, only the foreign producer price index was identified as a cause for cash-based exports. These findings illustrate that economic indicators, which may pose risks for Turkish exporters, are reasons for important decisions. In light of these findings, recommendations have been made for exporters and policymakers to proceed cautiously in the face of different economic scenarios.

Simulation and Attribution Analysis of Spatial–Temporal Variation in Carbon Storage in the Northern Slope Economic Belt of Tianshan Mountains, China

Intensive economic and human activities present challenges to the carbon storage capacity of terrestrial ecosystems, particularly in arid regions that are sensitive to climate change and ecologically fragile. Therefore, accurately estimating and simulating future changes in carbon stocks on the northern slope economic belt of Tianshan Mountains (NSEBTM) holds great significance for maintaining ecosystem stability, achieving high-quality development of the economic belt, and realizing the goal of “carbon neutrality” by 2050. This study examines the spatiotemporal evolution characteristics of the NSEBTM carbon stocks in arid regions from 1990 to 2050, utilizing a combination of multi-source data and integrating the Patch-generating Land use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. Additionally, an attribution analysis of carbon stock changes is conducted by leveraging land use data. The findings demonstrate that (1) the NSEBTM predominantly consists of underutilized land, accounting for more than 60% of the total land area in the NSEBTM. Unused land, grassland, and water bodies exhibit a declining trend over time, while other forms of land use demonstrate an increasing trend. (2) Grassland serves as the primary reservoir for carbon storage in the NSEBTM, with grassland degradation being the leading cause of carbon loss amounting to 102.35 t over the past three decades. (3) Under the ecological conservation scenario for 2050 compared to the natural development scenario, there was a net increase in carbon storage by 12.34 t; however, under the economic development scenario compared to the natural development scenario, there was a decrease in carbon storage by 25.88 t. By quantitatively evaluating the land use change in the NSEBTM and its impact on carbon storage in the past and projected for the next 30 years, this paper provides scientific references and precise data support for the territorial and spatial decision making of the NSEBTM, thereby facilitating the achievement of “carbon neutrality” goals.

Network slicing: Is it worth regulating in a network neutrality context?

Network slicing is a key component of 5G-and-beyond networks but induces many questions related to an associated business model and its need to be regulated due to its difficult co-existence with the network neutrality debate. We propose in this paper a slicing model in the case of heterogeneous users/applications where a service provider may purchase a slice in a wireless network and offer a “premium” service where the improved quality stems from higher prices leading to less demand and less congestion than the basic service offered by the network owner, a scheme known as Paris Metro Pricing. We obtain thanks to game theory the economically-optimal slice size and prices charged by all actors. We also compare with the case of a unique “pipe” (no premium service) corresponding to a fully-neutral scenario and with the case of vertical integration to evaluate the impact of slicing on all actors and identify the “best” economic scenario and the eventual need for regulation.

Ecological Risk Assessment of Land Use Change in the Tarim River Basin, Xinjiang, China

In recent years, global climate change and human alterations to land use have led to a decrease in ecosystem services, making ecosystems more vulnerable. However, unlike the well-established risk assessment frameworks used in natural disaster research, the concept of ecological risks arising from changes in land use is still in its early stages, with its nuances and assessment methodologies yet to be clearly defined. This study proposes a new framework for assessing ecological risks resulting from changes in land use in the Tarim River Basin. The framework employs a coupled PLUS and Invest model to evaluate the ecological risks of land use change under three development scenarios projected for the Tarim River Basin in Xinjiang by 2035. The findings indicate that: (1) Between 2000 and 2020, the predominant land use types in the Tarim River Basin in Xinjiang were primarily unused land, followed by grassland and cropland. Conversely, grassland, water, and construction land were relatively less prevalent. During this period, the area of unused land and cultivated land increased, while grassland, forest land, and water exhibited a declining trend. Moving forward, under the three scenarios from 2020 to 2035, land use changes in the study area are characterized by the expansion of cropland and unused land, coupled with a significant decrease in grassland area, while other land categories demonstrate minor fluctuations. (2) From 2020 to 2035, across various scenarios, the total ecosystem service within the study area demonstrates an overall increasing trend in both the northern and southern marginal zones. Specifically, under the baseline scenario, the total amount of ecosystem services in the study area decreased by 15.247% compared to 2020. Similarly, under the economic development scenario, this decrease amounted to 13.358% compared to 2020. Conversely, under the ecological protection scenario, the decrease reached 19.852% compared to 2020. (3) The structure of ecological risk levels from 2020 to 2035, across multiple scenarios, demonstrates a consistent pattern, characterized by a predominant proportion of moderate risk. Conversely, other risk levels occupy relatively smaller proportions of the area.

GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau

Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.

Formation of economic indicators of national security in the context of business digitalization and changes in the economy

Abstract. Today, Ukraine's economic development is taking place in the context of market competition, negative phenomena in the foreign economic sphere, and most importantly, russia's full-scale military aggression. At the same time, economic relations in the country are characterized by such undesirable phenomena as the shadow economy, economic inequality of business participants, growth of external debts, decline in economic activity, deficits of the state and territorial budgets, unprofitability of business entities, migration processes, unemployment, and poverty. The article examines the content of the concept of economic security in the context of the national security component, which is determined by a set of tasks based on the implementation of the Economic Security Strategy of Ukraine for the period up to 2025. The author analyzes the dynamics of indicators that determine the state of economic security of Ukraine and the existing threats in the national economy. In order to monitor the state of economic security in accordance with the Strategy, it is proposed to fulfill the task of its scientific support, to develop methodological recommendations and a mechanism for interpreting the integral indicator. At present, there is no information on the level of economic security of Ukraine on the official websites of the legislative and executive branches of government since 2021. The system of reservations regarding the indicators for assessing the economic security of Ukraine proposed in the Strategy is substantiated. A simplified list of indicators for monitoring the state of economic security is defined and interval signs of economic development scenarios at the level of optimistic, pessimistic and basic characteristics are developed for the integral indicator. The assessment of the status of economic security objects (the State, territorial communities, types of economic activity, business entities) is significantly influenced by the development of digitalization of the economy. It is established that in our country the processes of digitalization do not have a cross-cutting connection «business entities – types of economic activity – territorial communities – the State», but arise and develop locally within individual business and government structures.

Parametric simulation tool for the enviro-economic evaluation of energy renovation strategies in residential buildings with life cycle thinking: PARARENOVATE-LCT

The EU regulations require long-term renovation strategies (LTRS) for the energy transition of the existing building stock, emphasising the importance of the whole-life carbon evaluation and the cost-effectiveness of the processes. The building performance simulation (BPS) tools combined with the integration of the life cycle thinking (LCT) are considered a promising approach for evaluating the renovation of buildings. Besides, the performance gap due to occupant behaviour (OB) and the uncertainty of the economic scenario is an essential barrier to the accurate assessment of renovation strategies in residential buildings. The objective of this study is to develop a simulation tool to evaluate the environmental and economic sustainability of energy renovations in residential buildings based on LCT, considering the OB diversity and economic uncertainty. For this, the study develops the PARARENOVATE-LCT tool. This script-based tool combines the dynamic BPS and the integration of the environmental life cycle assessment (LCA) and the economic life cycle cost (LCC), evaluating different renovation strategies in multiple scenarios based on the combination of multiple variables of archetype, climatic zone, OB patterns, economic scenario and orientation. The methodological tool is applied in a case study of the most common archetypes of single-family and multi-family residential buildings of the Basque Country, in northern Spain. The results show how the tool can prioritise strategies, analyse the significance of baseline scenario parameters, and measure the influence of the LCT application. This way, the study provides a methodological tool as a new approach towards developing policies and plans to answer the EU requirements.

PECULIARITIES OF OPTIMIZATION OF TEACHING MATHEMATICS TO STUDENTS OF ECONOMIC SPECIALTIES IN HIGHER EDUCATION INSTITUTIONS

In the article, the author studies the peculiarities of optimizing the teaching of mathematics to students of economic specialties in higher education institutions. It has been found that teaching mathematics to students of economic specialties is accompanied by a number of problems, in particular, the subject area, the peculiarities of the modern educational environment and the social context. Some students do not show sufficient interest and do not realize the importance of mathematics in professional activities. Low motivation leads to the lack of concentration in the classroom and worsening the outcomes. The quality of mathematics education is negatively affected by the lack of a sufficient level of students’ basic mathematical knowledge. They may not understand real economic scenarios of future professional activities and have difficulty using mathematical concepts to model and analyze real economic situations. Some mathematical concepts and categories, such as differential equations or statistics, can be difficult to understand and apply in the context of complex economic models.Optimization of teaching means bringing this process to a state that ensures the achievement of goals in accordance with the requirements for the content and level of professional training of students, taking into account their interests and capabilities in conditions of limited use of time and effort resources. The main ways to optimize mathematics teaching are: use of specific economic examples and tasks to explain mathematical concepts, because learning can be more effective if students see how they can use mathematics in their future economic activities; integrating teaching with economic models and mathematical concepts so that students can see the use of math in analyzing economic phenomena; use of computer and specialized programs for visualization of mathematical concepts and their use in economic issues; introduction of interactive online resources to facilitate the learning process; active use of applied tasks, inclusion in the curriculum of tasks that require the use of math skills to solve specific economic problems, etc.

A Multi-Objective Scenario Study of County Land Use in Loess Hilly Areas: Taking Lintao County as an Example

Land use serves as a connecting link between human activities and the natural ecology of the surface; under the multi-objective background of national policies and dual-carbon tasks, land use transformation is studied and simulated in multiple scenarios, and carbon stock changes are analyzed based on future land use to explore the path for a region to achieve multi-objective coordination. Drawing upon land use data from 2000 to 2020 in Lintao County, Gansu Province, we conducted an in-depth analysis of the dynamics governing land use transformation. Subsequently, employing the FLUS (Future Land Use Simulation) model, we simulated the projected land use for Lintao County in 2035 under various scenarios. Furthermore, we utilized the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model to assess the change in carbon stock within the study area under each scenario. These analyses aim to furnish a robust scientific foundation for future land use planning endeavors in Lintao County. The conclusions are as follows: (1) The land use transition in Lintao County from 2000 to 2020 showed the strongest motivation for construction land growth, with continued rapid growth in the scale of urban land and other construction land and relatively slow growth in the land for rural settlement areas, while cropland and water areas continued to decrease, forest land grew slowly, the magnitude of land use change exhibited a higher intensity in river townships compared with mountainous townships. (2) The simulation results of cropland protection scenario (CPS), ecological protection scenario (EPS), economic development scenario (EDS), and comprehensive development scenario (CDS) in 2035 are better. Among them, the CDS, which considers various types of higher-level strategic requirements and can compensate for the single-goal nature of the single-demand scenario, demonstrates a higher level of rationality in the land use pattern. (3) The total carbon stock in descending order is the EPS, CDS, EDS, and CPS. Among these, the CDS is at a higher level of total carbon stock, and the changes in carbon stock in each land use site are more balanced, which is an ideal carbon stock state and a scenario more in line with multi-objective coordination.

Forecasting carbon peaking in China using data-driven rule-base model: An in-depth analysis across regional and economic scenarios

At the 2020 United Nations Climate Summit, China officially announced the goal to achieve carbon peaking by 2030. Exploring whether it is possible to reach the peak of carbon emissions earlier necessitates an urgent and imperative need for precise long-term forecasting of China's carbon emissions dynamics. However, the current carbon peaking predictions mostly depend on mechanical or mathematical models, which failed to consider the interdependence between carbon emissions and the time series-based patterns existed in carbon emission data. Therefore, this study presents a novel carbon peaking prediction method based on the data-driven rule-base model, which is implemented by the adaption of the extended belief rule base (EBRB) model for time series forecasting (TSF), and thus the proposed method is referred to as TSF-EBRB model. The TSF-EBRB model not only captures and measures the temporal correlations within the data throughout the processes of modeling and inference, but also consists of a novel parameter optimization model based on the temporal correlations. The study collected carbon emission data from 30 provinces in China for empirical analysis. It computed and predicted the carbon peaking trajectories of each province under three different scenarios from 2022 to 2030, validating the effectiveness and superiority of the TSF-EBRB model better than other existing carbon peaking prediction methods. The results indicated that, with policy interventions, the majority of provinces are projected to reach carbon peaking before 2030.

Signature-based validation of real-world economic scenarios

Abstract Motivated by insurance applications, we propose a new approach for the validation of real-world economic scenarios. This approach is based on the statistical test developed by Chevyrev and Oberhauser ((2022) Journal of Machine Learning Research, 23(176), 1–42.) and relies on the notions of signature and maximum mean distance. This test allows to check whether two samples of stochastic processes paths come from the same distribution. Our contribution is to apply this test to a variety of stochastic processes exhibiting different pathwise properties (Hölder regularity, autocorrelation, and regime switches) and which are relevant for the modelling of stock prices and stock volatility as well as of inflation in view of actuarial applications.

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

In the dynamic global trade environment, accurately predicting trade values of diverse commodities is challenged by unpredictable economic and political changes. This study introduces the Meta-TFSTL framework, an innovative neural model that integrates Meta-Learning Enhanced Trade Forecasting with efficient multicommodity STL decomposition to adeptly navigate the complexities of forecasting. Our approach begins with STL decomposition to partition trade value sequences into seasonal, trend, and residual elements, identifying a potential 10-month economic cycle through the Ljung–Box test. The model employs a dual-channel spatiotemporal encoder for processing these components, ensuring a comprehensive grasp of temporal correlations. By constructing spatial and temporal graphs leveraging correlation matrices and graph embeddings and introducing fused attention and multitasking strategies at the decoding phase, Meta-TFSTL surpasses benchmark models in performance. Additionally, integrating meta-learning and fine-tuning techniques enhances shared knowledge across import and export trade predictions. Ultimately, our research significantly advances the precision and efficiency of trade forecasting in a volatile global economic scenario.

Cost-benefit analysis of a hydro-solar microsystem with Archimedean screw hydro turbine sized for a prosumer building

This work aims to identify a cost-effective solution for improving the energy performance of a grid-connected prosumer, possibly equipped with electric vehicle chargers. The solution is based on a small-scale energy system based on renewable energy sources (RES). The system is equipped with an Archimedes Screw Turbine (AST) and photovoltaic panels. Energy storage in batteries is also considered. A simple yet efficient method for sizing the AST is proposed. A cost-benefit analysis of the small-scale energy system is performed for six case studies covering different combinations of energy consumers. Three economic scenarios are considered for each case study. The results suggest that the AST-based solution leads to reasonable costs for the prosumer and less impact of energy price fluctuations. Also, if EV chargers are used, their consumption can be covered directly by RES.

Systems analysis for energy assets of Iraq influenced by water scarcity

AbstractWater security in arid and semi-arid Middle Eastern climates has been severely impacted by effects of climate change such as reduced precipitation, diminished storage, increased evapotranspiration, and prolonged heat waves. These climate effects are compounded in Iraq, where populations, agriculture, industry, and energy rely heavily on varying transboundary water flows to meet water demands. Iraq’s most profitable sector, energy, is especially threatened by insufficient water supply, which complicates government decision making in energy infrastructure development. The goal of this paper is to develop a scenario-based multi-criteria analysis framework to prioritize infrastructure investments in the context of climate change and scarcity of natural resources. Infrastructure facilities are evaluated against social, economic, climate, and hydrologic criteria across a set of disruptive climatological, economic, and social scenarios to identify robust initiatives and the most and least disruptive scenarios to the system. A particular innovation of this paper is the evaluation of hydrology data derived from satellites in determining water scarcity impact on individual energy facilities. The methods are demonstrated for a critical sector of Iraq’s economy: oil and natural gas. The demonstration includes 13 system-order criteria, 44 oil and gas initiatives within Iraq, and seven risk scenarios. The results include an accounting of the most and least disruptive scenarios to energy sector priorities and scenario-based system orderings to guide stakeholders in investment prioritization.

A spatial decision support system for multi-dimensional sustainability assessment of river basin districts: the case study of Sarno river, Italy

Effective management of the complex Socio-ecological systems (SES) of the river basin, influenced by a range of social, economic, and environmental variables, requires multi-purpose Spatial Decision Support Systems (SDSSs) to identify and select balanced and sustainable regeneration scenarios. In many cases, the lack of cooperation between administrative institutions and the low level of participation of the stakeholders involved are the main obstacles to effective planning and regeneration practices and call for new forms of governance. However, to address these critical issues, few studies use quantitative approaches to assess the multidimensional resources of river basins in an integrated perspective in terms of their capacity to activate cooperation networks between river-crossed cities. This study aims to suggest a methodological approach combining Multi-Criteria Decision Analysis and Geographic Information Systems to achieve integration in pursuing sustainable regeneration pathways for a river basin district, analysing the case study of the Sarno River, composed of 20 administrative units, in Southern Italy. The specific purpose is to provide decision-makers with an SDSS to evaluate sustainability scenarios based on the multi-dimensional performances of River Basin District Units (RBDU). The results delivered four partial rankings of RBDU related to social, cultural, ecological, and economic scenarios and one overall ranking for an integrated sustainability scenario. Each ranking includes a clustering of municipalities according to three classes: Leader, Link and Follower. The outcome is twofold: to provide decision-makers with recommendations for supporting governance models based on sustainability pathways and to suggest valuable methods and tools for dealing with the spatial misfits, considered as the mismatch between the physical boundaries of a natural system and the management area of an organization.

Global enhanced oil recovery assessment: A comprehensive data-driven analysis using self-organizing maps

This study presents a comprehensive analysis of Enhanced Oil Recovery (EOR) methods' application in the global oil industry, identifying trends and main factors influencing their adoption. A wealth of unstructured and scattered data from public documents covering traditional EOR methods and newer technologies was processed to create a reliable global database, encompassing 1237 EOR projects in reservoirs around the world. Data science techniques and self-organizing maps (SOM) were employed for data analysis, dimensionality reduction, and visualization of complex problems, proving suitable for high-dimensionality and incomplete data. The research integrates various data modalities and data science visualization techniques, enabling a high-level representation of EOR projects' self-contained knowledge, and providing a comprehensive view of EOR application worldwide. The study reveals implicit interrelationships between multiple variables and EOR methods, identifying trends, research and development directions, and better-informed decisions. In-depth analysis showed that factors impacting the choice, application, and success of EOR methods include technical properties of reservoirs, local, commercial, and temporal aspects. Despite the diffusion of secondary and tertiary recovery technologies, a concentration of EOR methods exists in specific regions. Factors influencing EOR projects' implementation, maintenance, and expansion range from technical reasons, deselected reserves, and infrastructure to incentive policies and regulatory aspects of each country, business structure, and global economic scenario. Both petrophysical and non-petrophysical variables influence the application of EOR methods. Availability and costs of purchasing and transporting injection fluids greatly impact decision-making processes. The analysis contributes to understanding the complex network of factors influencing EOR, and this work can aid in making more informed decisions in the industry and research for EOR applications.

Comparative Life Cycle Assessment of Battery and Fuel Cell Electric Cars, Trucks, and Buses

Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy-duty trucks, and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e.g., the conversion and storage of surplus renewable electricity via electrolysis, become important.

Life Style Changes for Environment and Sustainable Development

In the changing world economy scenario and due to consumerism name became a major factor for industry to make a product and services as per customer choice. This consumerism impacted on a large scale in the competitive market due to increase in purchasing power people are demanding new product with special features and corporate houses are somehow compromising on sustainability of development resulting in fast and rapid exploitation of natural resources although government name imposed several restriction on consumer market like ban on plastic, ban on wildlife product, less use of germicide and pesticide in packet foods, waste disposal system at every industry, ban on excessive use of water resources, ban on old vehicle use of green energy, bio fuel, solar energy etc.

Research on driving mechanism and prediction of electric power carbon emission in Gansu Province under dual-carbon target

The electric power industry is a key industry for the country to achieve the double carbon target. Its low carbon development has a double effect on this industry and helps other industries to achieve the carbon peak target. This paper firstly uses the IPCC inventory method to calculate carbon emissions in the production phase of the power industry in Gansu Province from 2000 to 2019, followed by the ridge regression method and the STIRPAT model to analyse the quantitative impact of six major drivers on carbon emissions, and finally, the scenario analysis method is used to forecast carbon emissions in this phase. The results show that the carbon emissions of Gansu Province show a trend of rising and then falling, and reached a peak of 65.66 million tons in 2013. For every 1% increase in population effect, urbanisation level, affluence, clean energy generation share, technology level and industrial structure, carbon emissions will grow by 4.939%, 0.625%, 0.224%, − 0.259%, 0.063% and 0.022% respectively. Because of the clean energy advantage in Gansu Province, the low-carbon development scenario will continue to have low carbon emissions during the scenario cycle, which can be reduced to 53.454 million tons in 2030; the baseline scenario will achieve a carbon peak in 2025, with a peak of 62.627 million tons; the economic development scenario has not achieved carbon peak during the scenario cycle, and carbon emissions will increase to 73.223 million tons in 2030.