An Innovative Digital Platform for Socioeconomic Forecasting Climate Risks and Financial Management

Abstract

This article presents an innovative methodology for enhancing statistical databases as reliable sources of information. The study leverages data from “Big Data of the Modern Global Economy: A Digital Platform for Data Mining—2020”, which serves as a digital tool designed to predict economic development at both global and national levels, particularly in the context of the COVID-19 crisis and its aftermath. Utilizing a dataset focused on the G7 and BRICS nations as a case study, we assemble forecasts for several key indicators: the Digital Competitiveness Index, Global Innovation Index, Human Development Index, Gross Domestic Product (GDP), Economic Growth Rate, GDP per Capita, Quality of Life Index, Happiness Index, and Sustainable Development Index for 2021. Additionally, we conducted a plan-fact analysis. The accuracy of the post-pandemic economic recovery forecast is validated through comparison with actual data. Furthermore, this research provides statistical analyses and forecasts to minimize uncertainty during crises, considering the interconnected nature of climate change and financial factors inherent in these crises.

1. Introduction

This research aimed to improve forecasting methods during uncertain and risky economic times, particularly during crises like COVID-19. The COVID-19 crisis has brought about significant changes in the global economy (Asem et al., 2021) and highlighted the limitations of forecasting techniques, which contributed to prolonged economic damage worldwide. Learning from this experience is essential for enhancing states’ and corporations’ socioeconomic forecasting and economic planning.

Following a protracted recovery from the 2008 financial crisis, the global economy exhibited stability in late 2019 and early 2020, which fostered optimistic projections influenced by four significant trends observed in recent years. The first tendency represents the transition to the digital economy and Industry 4.0, which led to the dissemination of the leading technologies and increased competition for hi-tech products (Popkova & Gulzat, 2020a). The second tendency is the growing role of the knowledge economy (Popkova & Gulzat, 2020b). The third tendency focuses on economic development and enhanced public well-being, whereby living standards are key to a country’s competitiveness for attracting and retaining skilled personnel and investments (Popkova & Zmiyak, 2019). Fourth, adopting sustainable development goals in 2015 established new criteria for evaluating economic systems, among others, the quality of life, and creating a socially oriented (humanistic) economy (Popkova & Sergi, 2019).

The COVID-19 crisis has caused economic instability, highlighting the importance of evaluating its effects and advancing alternative growth scenarios (Papava, 2020; Piserà & Chiappini, 2024; Popkova, 2019; Popkova et al., 2019; Popkova & Parakhina, 2016). To this end, it is essential to resort to comprehensive statistics that include a variety of indicators to predict the trajectory of future crises and identify their contributing factors. However, existing research relies on a limited selection of countries and/or focuses solely on particular indicators (Bibri, 2018; Burkart et al., 2016). Some other datasets on the global economy, including Chen et al. (2020) and Emmanuel et al. (2021), are also specialized and fragmentary.

Working with COVID-19 data, the methodology automates data collection and leverages Big Data to accurately forecast global economic development amid instability. A case study will be conducted to evaluate the dataset “Big Data of the Modern Global Economy: A Digital Platform for Data Mining—2020” using data from G7 and BRICS countries: IMD (2025): World Digital Competitiveness Ranking 2019, International Monetary Fund (2025): World Economic Outlook Database, Numbeo (2025): Quality of Life Index for Country 2019 Mid-Year, UN: World Happiness Report 2019 (UNDP, 2025a), Human Development Report 2019 (UNDP, 2025b), UN (2025): Sustainable Development Report 2019, WIPO (2025): The Global Innovation Index—2019, World Bank (2025): Indicators and World Economic Forum (2025): The Global Competitiveness Report 2019.

This paper presents a socioeconomic forecasting related to climate risks and financial management in the face of uncertainty. The novelty of this research lies in its re-evaluation of financial risk management through an innovative digital platform that focuses on socioeconomic forecasting regarding climate risks and financial management. This paper explores anti-crisis management theory and aims to highlight its transformative potential. Our research question is: Can economic forecasts concerning transformations in crisis-affected systems be improved by analyzing historical stability data? The analysis will investigate the interrelationships among Industry 4.0, the knowledge economy, economic growth, and sustainable development, considering countries with varying income levels.

2. Literature Review

The COVID-19 pandemic crisis and other socioeconomic shocks and factors (e.g., expenditures for medical services and healthcare, organization on the resolution of crises, etc.) can help/hinder countries from coping with these environmental threats described in Çelik (2016), Culyer et al. (2018), de la Barrera (2016), Gorman (2016), Laatifi et al. (2022), Londoño Díaz and Prado Mejía (2021), Luo (2019), Martínez-Campillo and Fernández-Santos (2020), H. D. Nguyen et al. (2021), Odoardi and Muratore (2019). These authors highlight the significant capabilities of economic crisis management during the COVID-19 pandemic. Part of the literature (Belaïd & Amine, 2025; Srivastava et al., 2025) correlates the onset of the COVID-19 pandemic with global climate change.

Battaglia et al. (2024), Cardillo and Chiappini (2022), Desogus et al. (2024), Ergasheva et al. (2023), Fiorillo et al. (2024), Kantor et al. (2023), Maharana et al. (2025), Piserà and Chiappini (2024), Setiawan and Septiani (2025), and Zedda et al. (2021) investigate financial risk management strategies in response to crises of a non-economic nature, such as pandemics and climate-related challenges.

Current literature suggests utilizing fragmented statistical data, often relying on limited country samples and isolated indicators (Coccia, 2021). Cui et al. (2023), Dubey et al. (2024), Kordestani et al. (2023), Kumar et al. (2023), T. L. Nguyen et al. (2023), Obreja et al. (2024), Sarraf et al. (2024) and Sharma et al. (2023) detail using digital technologies, including big data and AI, for dataset analytics. While they outline technical capabilities, they do not address the methodological issues in applying these technologies to forecasting.

Drawing on the research of Feng et al. (2024), Fertő and Harangozó (2025), Horvey and Odei-Mensah (2025), and Xie et al. (2024), we propose that the development of Industry 4.0, the knowledge economy, economic growth, and sustainable development are closely interconnected and have a synergistic effect on climate adaptation, with the scale of this effect varying among nations with different income levels. Given the shortcomings of current forecasting methods in effectively managing crisis uncertainty, it seems that scenario analysis could be our most powerful tool for navigating the unknown (Jihan et al., 2025). However, forecasts from this method remain vague and rough regarding which scenarios will materialize (Berti et al., 2025). The “other conditions being equal” forecasting method is largely ineffective as it fails to account for a new status quo (Guan et al., 2025; Mislan & Dani, 2025) and instead relies on insufficient statistics, resulting in primarily qualitative forecasts without numerical indicators (Martin et al., 2025; Wach et al., 2025). The dataset analytics method addresses the limitations of previous approaches by leveraging extensive data. Although practical application during crises requires foresight elements (Lee et al., 2025; Yhang et al., 2025), a gap exists in applying these elements to managerial decision-making during economic crises, as they often become irrelevant.

3. Methodology

3.1. Sample and Data

This research draws on “Big Data of the Modern Global Economy: A Digital Platform for Data Mining—2022” for various categories of countries. When the ISC was forecasting 2020–2024, the World Bank classification applied. The World Bank annually updates the four groups of countries (framework values of GDP per capita for each category)—we go by the latest classification for 2020:

• Countries with low GDP per capita (Low income): less than USD 1026;
• Countries with below-average GDP per capita (Lower-middle income): USD 1026–3995;
• Countries with above-average GDP per capita (Upper-middle income): USD 3996–12,375;
• Countries with high GDP per capita (High income): more than USD 12,375.

This paper compares high-income nations, exemplified by the G7, and the BRICS countries, which boasted above-average GDP per capita in 2020. The forecasting model is structured as follows:

α = β × δ

(1)

• α—the predicted value of the indicator in the country that belongs to category i;
• β_i—the factual basic value of the indicator in a country that belongs to category i;
• δ—forecast coefficient for the category of countries i.

Model (1) compares forecasts derived from dataset analysis. It takes into account the synergistic effects of Industry 4.0, the knowledge economy, economic growth, and sustainable development across countries with varying income levels, as displayed by correlation coefficients. This empirical model enables plan-fact analysis, making it effective for comparing forecasts with real economic data and highlighting the forecasting potential of datasets. The model improves over time as forecasting experience is gained, leading to more accurate coefficients.

The empirical model (1) aims to adjust a country’s factual statistical data using a prognostic coefficient established through expert analysis. Model (1) generates forecasts for distinct groups of countries and allows for comparing expected and actual changes in socioeconomic indicators among countries from other groups. This identifies which countries manage to navigate crises effectively, which are most adversely affected, and how well each country develops its anti-crisis management potential.

Forecasting coefficients are initially derived from correlation analysis, followed by regression analysis. To account for the interrelationships between indicators and to model their dynamic variability, the mathematical model (1) is supplemented with model (2):

α_j1 = b + ∑_k=2…8c × α_jk

(2)

• α_j1—any variable from

  Table 1. Forecasting logic and annual growth rate indicators categorized by country.

  Indicator	Forecast of the Annual Growth Rate of the Indicator
  	Countries with Low GDP per Capita	Countries with Below-Average GDP per Capita	Countries with Above-Average GDP per Capita	Countries with High GDP per Capita
  Industry 4.0:	Slow	accelerated	rapid	very rapid
  Digital Competitiveness Index	not subject to calculation	×1.015	×1.02	×1.025
  World Competitiveness Index 4.0	×1.01
  Knowledge Economy:	Slow	accelerated	rapid	very rapid
  Global Innovation Index	×1.01	×1.015	×1.02	×1.025
  Human Development Index
  Economic growth *	Slow	rapid	very rapid	accelerated
  GDP *	×1.01	×1.02	×1.025	×1.015
  Rate of economic growth *
  GDP per capita *
  Sustainable development:	Slow	accelerated	very rapid	rapid
  Quality of Life Index	×1.01	×1.015	×1.02	×1.025
  Happiness Index
  Sustainable Development Index

  * The ISC forecast is only made from 2023 to 2024, while the IMF’s forecast data are presented for 2019–2022).

  ;
• b—constant;
• c—regression coefficient with each variable in Table 1, apart from α_j1;
• j—sequence number of the variable from Table 1;
• k—total number of factor variables (8), which are considered in the model (2), from

  Table 2. Factual data from the G7 and BRICS in 2019 and 2021.

  Country	Digital Competitiveness Index, Score 1–100	Global Competitiveness Index 4.0, Score 1–100	Innovation Index, Score 1–100	Human Development Index, Share of 1	Economic Growth Rate, %	GDP, Billion Dollars	GDP per Capita, Dollars	Quality of Life Index, Score 1–200	Happiness Index, Score 1–10	Sustainability Index, Score 1–100
  Major Advanced Economies—G7 (developed countries): 2019 factual data
  Canada	90.836	79.6	53.88	0.922	1.843	1719.451	45,845.251	169.42	7.278	77.9
  France	82.522	78.8	54.25	0.891	1.749	2562.275	39,121.158	156.10	6.592	81.5
  Germany	86.216	81.8	58.19	0.939	1.415	3617.086	43,372.885	184.30	6.985	81.1
  Italy	67.903	71.5	46.30	0.883	0.800	1879.410	30,941.744	143.81	6.223	75.8
  Japan	82.775	82.3	54.68	0.915	0.846	5085.741	40,542.057	176.46	5.886	78.9
  UK	88.691	81.2	61.30	0.920	1.606	2607.850	38,965.146	166.73	7.054	79.4
  USA	100.000	83.7	61.73	0.920	2.121	21,239.303	64,212.535	176.77	6.892	74.5
  BRICS (developing countries): 2019 factual data
  Brazil	57.346	60.9	33.82	0.761	1.954	2340.842	11,110.946	103.87	6.300	70.6
  China	84.292	73.9	54.82	0.758	6.000	13,862.968	9850.988	99.87	5.191	73.2
  India	64.952	61.4	36.58	0.647	7.791	2959.667	2173.500	115.41	4.015	61.1
  Russia	70.406	66.7	37.62	0.824	1.500	1654.091	11,558.835	104.05	5.648	70.9
  South Africa	60.865	62.4	34.04	0.705	2.198	339.846	5790.651	135.75	4.722	61.5
  Major Advanced Economies—G7 (developed countries): 2021 factual data
  Canada	87.310	n/a	53.1	0.929	5.688	2015.983	52,791.228	157.25	7.103	79.16
  France	75.656	n/a	55.0	0.901	6.293	2940.428	45,028.265	153.60	6.690	81.47
  Germany	79.334	n/a	57.3	0.947	3.051	4230.172	50,787.859	175.24	7.155	82.48
  Italy	61.767	n/a	45.7	0.892	5.770	2120.232	35,584.882	137.77	6.483	78.76
  Japan	73.014	n/a	54.5	0.919	2.358	5103.110	40,704.304	164.06	5.940	79.85
  UK	85.827	n/a	59.8	0.932	6.763	3108.416	46,200.258	156.94	7.064	79.97
  USA	100.00	n/a	61.3	0.926	5.973	22,939.580	69,375.375	163.60	6.951	76.01

  .

The model (2) is formulated for each of the nine variables specified in Table 1, facilitating a system of equations for multiple linear regression analysis. Comprehensive regression statistics are provided for each equation to prove model reliability (2).

3.2. Measures of Variables

The interconnectedness of research data yields a synergistic effect on countries’ climate adaptation, with observable variations contingent on income categories. Statistical analyses will project developments during the COVID-19 pandemic, emphasizing climate risks from 2020 to 2024. Industry 4.0 encompasses both the digital economy and competitiveness. The knowledge economy focuses on innovation and human development, recognizing humans as the primary carriers of knowledge. Economic growth is reflected in GDP, GDP per capita, and growth rate. Sustainable development includes quality of life, societal happiness, and implementing the Sustainable Development Goals (SDGs). Table 1 delineates the forecasting methodology and annual growth rates of indicators, organized by country, according to the ISC framework.

The International Standards Council (ISC) has established the coefficients presented in Table 1 through an expert methodology that considers current trends in the global economy and analytical reports from pertinent international organizations. These organizations include the UN, which has numerous conventions, agreements, and programs about global economic growth; the World Bank, which provides insights in its Global Economic Prospects; and the International Monetary Fund, which publishes the World Economic Outlook (January 2020). Additionally, PwC offers projections in its Global Economy Watch, and CNBC features charts that illustrate the latest IMF forecasts for the global economy as of Davos 2020.

3.3. Approach and Data Analysis Procedure

To formulate a forecast, we analyzed the period from 2020 to 2024, considering it a conditional phase of global development. During this time frame, the International Statistical Classification (ISC) suggests a probable increase in the values of the indicators under consideration for the selected categories of countries, with such growth aligning with designated coefficients. Countries have been assigned specific coefficients based on classifications established by the World Bank, categorizing nations into four distinct groups. The actual values of these indicators from previous periods have informed the coefficients applied to all indicators within their respective country categories. The indicators provided in the dataset from earlier periods are publicly accessible, including those of IMD, WEF, UNDP, Numbeo, UN, and Sustainable Development Solutions Network. The projected GDP, GDP per capita, and the economic growth rate for 2020–2022 have been taken from the International Monetary Fund’s official forecast.

4. Results

The innovative digital platform “Big Data of the Modern Global Economy: A Digital Platform for Data Mining—2020” is available in English and Russian and includes a comprehensive dataset with information from 2020 and projections extending to 2024. Users can access the necessary statistics interactively through five consecutive stages.

The first stage allows users to select one or more country group templates, facilitating quick and simplified data analysis. This preliminary stage offers the following country templates: Major Advanced Economies (G7), BRICS, CIS, and EAEU. Alternatively, users can work with the entire dataset if templates are unnecessary.

In the second stage, a list of countries (according to the selected template) or a complete list of countries (according to the International Monetary Fund) is opened. The user can conveniently choose all indicators (with one click) and cancel the selection (“clear all”).

In the third step, indicators from the offered list are selected. Indicators are conveniently grouped according to four global economic development tendencies before the COVID-19 crisis.

Part “Industry 4.0” contains the following indicators:

• Digital Competitiveness Index;
• Global Competitive Index 4.0.

Part “Knowledge economy” has the following indicators:

• Innovation Index;
• Human development index.

Part “Economic growth” contains the following indicators:

• Rate of economic growth;
• GDP;
• GDP per capita.

Part “Sustainable development” contains the following indicators:

• Quality of life index;
• Index of happiness;
• Sustainable development index.

As in the prior stages, the user is afforded the option to select all indicators or to cancel the selection if desired. At each stage of the process, the user may verify the accuracy of their choices. A window at the bottom of the screen displays the options selected in earlier stages.

In the fourth stage, time rows (calendar years) are selected. The factual data for 2019 and forecast data for 2020–2024 are available. Users could choose any time row, but it is not necessarily in strict order. One click could select all periods; it is possible to cancel the selection.

The fifth step generates a table from the selected data, organized in various ways: sorted in ascending order with the first click, descending order with the second click, alphabetically by country when clicking on the “Country” column, or by the values of any specific indicator by clicking on the respective indicator. Additionally, users can rank the data either by indicator or by country. Statistics of the global economy indicators by the G7 and BRICS in 2019 and a forecast of “other conditions being equal” for 2020 and 2024 (selection of data from the dataset) are shown in Table 2 and

Table 3. Factual data from the G7 and BRICS in 2021 and forecasts for 2024 under unchanged conditions.

BRICS (Developing Countries): 2021 Factual Data
Brazil	51.478	n/a	34.2	0.765	5.229	1645.837	7741.153	104.70	6.330	71.34
China	84.431	n/a	54.8	0.761	8.015	16,862.979	11,891.202	103.16	5.339	72.06
India	55.126	n/a	36.4	0.645	9.497	2946.061	2116.444	103.00	3.819	60.07
Russia	60.271	n/a	36.6	0.824	4.690	1647.568	11,273.242	97.91	5.477	73.75
South Africa	43.641	n/a	32.7	0.709	5.000	415.315	6861.166	131.37	4.956	63.74
Major Advanced Economies—G7 (developed countries): forecast (other conditions being equal) for 2024
Canada	103.124	90.4	61.17	1.047	1.854	1970.620	50,845.660	192.34	8.263	88.4
France	93.685	89.5	61.59	1.012	1.914	2900.437	43,681.620	177.22	7.484	92.5
Germany	97.879	92.9	66.06	1.066	1.208	4042.000	48,433.690	209.23	7.930	92.1
Italy	77.088	81.2	52.56	1.002	0.876	2053.830	33,858.150	163.26	7.065	86.1
Japan	93.972	93.4	62.08	1.039	0.657	5530.441	44,660.940	200.33	6.682	89.6
UK	100.688	92.2	69.59	1.044	1.975	2960.219	43,403.150	189.28	8.008	90.1
USA	113.527	95.0	70.08	1.044	1.754	24,478.490	72,325.760	200.68	7.824	84.6
BRICS (developing countries): forecast (other conditions being equal) for 2024
Brazil	61.778	67.2	37.34	0.840	2.094	2811.758	13,092.080	114.68	6.956	77.9
China	93.065	81.6	60.53	0.837	5.989	18,603.030	12,988.590	110.26	5.731	80.8
India	69.972	66.1	39.41	0.697	5.621	1651.163	5917.128	124.33	4.325	65.8
Russia	77.734	73.6	41.54	0.910	1.576	1934.049	13,586.510	114.88	6.236	78.3
South Africa	67.200	68.9	37.58	0.778	2.334	399.684	6487.678	149.88	5.213	67.9

.

Case study of using a dataset on the G7 and BRICS

To exemplify the application of a dataset, it is recommended that users thoroughly examine the interrelations among indicators of sustainable development, with particular emphasis on quality of life, happiness, and sustainability. Additionally, users should explore other pertinent indicators within the dataset that may impact these key factors. We present the correlation analysis for the Group of Seven (G7) countries in Figure 1 and the BRICS countries in Figure 2.

As illustrated in Figure 1, only three factors examined demonstrate a consistent and non-contradictory positive correlation with all sustainable development indicators. These factors are quality of life, happiness, and sustainability. These factors include the global competitiveness index 4.0, which exhibits correlations of 87.13%, 20.82%, and −3.85%, respectively; the innovation index, with correlations of 72.19%, 43.58%, and −9.12%; and the human development index, which shows correlations of 86.18%, 57.21%, and 18.79%, respectively.

As illustrated in Figure 2, neither of the analyzed factors demonstrated a consistent and positive correlation with all dimensions of sustainable development, which include quality of life, happiness, and sustainability. Nevertheless, two factors emerge as particularly promising: the Human Development Index and GDP per capita.

The results from the correlation analysis, as depicted in Figure 1 and Figure 2, provide an initial interpretation of the data. To improve the forecasting model (1) and enhance its cognitive utility, a regression analysis will also be conducted employing the data from

Table 4. Dependence of α_j1 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9934
R-square	0.9869
Adjusted R-square	0.9519
Standard error	3.7281
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	3139.6802	392.4600	28.2377	0.0096
Residual	3	41.6953	13.8984
Total	11	3181.3756
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	−4.9146	45.1681	−0.1088	0.9202	−148.6597	138.8305
αj2	1.4566	0.5451	2.6722	0.0755	−0.2781	3.1912
αj3	167.4801	61.0078	2.7452	0.0710	−26.6740	361.6342
αj4	0.6522	1.2792	0.5099	0.6453	−3.4186	4.7231
αj5	0.0005	0.0004	1.0781	0.3599	−0.0009	0.0019
αj6	−0.0002	0.0001	−1.9740	0.1429	−0.0005	0.0001
αj7	−0.1671	0.1587	−1.0531	0.3696	−0.6720	0.3378
αj8	1.7496	3.1377	0.5576	0.6160	−8.2360	11.7353
αj9	−1.6591	1.0059	−1.6495	0.1976	−4.8602	1.5420

to facilitate further causal inference. We have expanded the mathematical model (1) by incorporating a second model (2) to account for the interrelationships among the indicators and models, as well as their dynamic variability. This enhancement is structured as a system of equations utilizing multiple linear regression (3).

$$\{\begin{array}{l}{\alpha }_{\mathrm{j}1}=-4.91 + {1.46\alpha }_{\mathrm{j}2}+{167.48\alpha }_{\mathrm{j}3}+{0.65\alpha }_{\mathrm{j}4}+{0.0005\alpha }_{\mathrm{j}5}-{0.0002\alpha }_{\mathrm{j}6}-{0.17\alpha }_{\mathrm{j}7}+{0.75\alpha }_{\mathrm{j}8}-{1.66\alpha }_{\mathrm{j}9},\\ \begin{array}{l}\begin{array}{l}{\alpha }_{\mathrm{j}2}=-19.14 + {0.48\alpha }_{\mathrm{j}1}-{85.68\alpha }_{\mathrm{j}3}+{0.25\alpha }_{\mathrm{j}4}-{0.00002\alpha }_{\mathrm{j}5}+{0.00008\alpha }_{\mathrm{j}6}+{0.15\alpha }_{\mathrm{j}7}-{1.74\alpha }_{\mathrm{j}8}+{1.23\alpha }_{\mathrm{j}9},\\ \begin{array}{l}{\alpha }_{\mathrm{j}3}=-0.006-{0.07\alpha }_{\mathrm{j}2}+{0.004\alpha }_{\mathrm{j}1}-{0.003\alpha }_{\mathrm{j}4}-{0.000002\alpha }_{\mathrm{j}5}+{0.0000009\alpha }_{\mathrm{j}6}+{0.001\alpha }_{\mathrm{j}7}-{0.004\alpha }_{\mathrm{j}8}+{0.010\alpha }_{\mathrm{j}9},\hfill \\ {\alpha }_{\mathrm{j}4}=28.60-{19.40\alpha }_{\mathrm{j}3}+{0.14\alpha }_{\mathrm{j}2}+{0.12\alpha }_{\mathrm{j}1}-{0.0003\alpha }_{\mathrm{j}5}+{0.00005\alpha }_{\mathrm{j}6}-{0.06\alpha }_{\mathrm{j}7}+{0.91\alpha }_{\mathrm{j}8}-{0.26\alpha }_{\mathrm{j}9},\hfill \end{array}\end{array}\hfill \\ \begin{array}{l}{\alpha }_{\mathrm{j}5}=\mathrm{62,239.57}-{1683.25\alpha }_{\mathrm{j}4}-{\mathrm{73,475.85}\alpha }_{\mathrm{j}3}-{76.43\alpha }_{\mathrm{j}2}+{579.35\alpha }_{\mathrm{j}1}+{0.22\alpha }_{\mathrm{j}6}-{109.71\alpha }_{\mathrm{j}7}+{1350.46\alpha }_{\mathrm{j}8}-{292.21\alpha }_{\mathrm{j}9},\hfill \\ {\alpha }_{\mathrm{j}6}=-\mathrm{112,988.16} + {2.33\alpha }_{\mathrm{j}5}+{3349.14\alpha }_{\mathrm{j}4}+{\mathrm{455,245.65}\alpha }_{\mathrm{j}3}+{3120.57\alpha }_{\mathrm{j}2}-{2694.54\alpha }_{\mathrm{j}1}-{110.97\alpha }_{\mathrm{j}7}+{1143.04\alpha }_{\mathrm{j}8}-{3029.78\alpha }_{\mathrm{j}9},\hfill \\ {\alpha }_{\mathrm{j}7}=157.07-{0.00008\alpha }_{\mathrm{j}6}-{0.0009\alpha }_{\mathrm{j}5}-{3.0474\alpha }_{\mathrm{j}4}+{394.21\alpha }_{\mathrm{j}3}+{4.41\alpha }_{\mathrm{j}2}-{1.61\alpha }_{\mathrm{j}1}+{9.05\alpha }_{\mathrm{j}8}-{6.44\alpha }_{\mathrm{j}9},\hfill \\ {\alpha }_{\mathrm{j}8}=-7.88 + {0.03\alpha }_{\mathrm{j}7}+{0.000003\alpha }_{\mathrm{j}6}+{0.000003\alpha }_{\mathrm{j}5}+{0.15\alpha }_{\mathrm{j}4}-{4.54\alpha }_{\mathrm{j}3}-{0.16\alpha }_{\mathrm{j}2}+{0.05\alpha }_{\mathrm{j}1}+{0.22\alpha }_{\mathrm{j}9},\hfill \\ {\alpha }_{\mathrm{j}9}=18.23 + {1.26\alpha }_{\mathrm{j}8}-{1.11\alpha }_{\mathrm{j}7}+{0.00004\alpha }_{\mathrm{j}6}-{0.00004\alpha }_{\mathrm{j}5}-{0.24\alpha }_{\mathrm{j}4}+{67.38\alpha }_{\mathrm{j}3}+{0.64\alpha }_{\mathrm{j}2}-{0.29\alpha }_{\mathrm{j}1}.\hfill \end{array}\hfill \end{array}\end{array}$$

(3)

The regression statistics detailed in Table 4,

Table 5. Dependence of α_j2 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9945
R-square	0.9890
Adjusted R-square	0.9597
Standard error	2.1478
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	1244.1910	155.5239	33.7143	0.0074
Residual	3	13.8390	4.6130
Total	11	1258.0300
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	−19.1450	23.6144	−0.8107	0.4769	−94.2964	56.0065
αj1	0.4834	0.1809	2.6722	0.0755	−0.0923	1.0592
αj3	−85.6822	43.4912	−1.9701	0.1434	−224.0906	52.7262
αj4	0.2485	0.7547	0.3293	0.7636	−2.1532	2.6503
αj5	−2.11 × 10−5	0.0003	−0.0696	0.9489	−0.0010	0.0009
αj6	8.06 × 10−5	8.028 × 10−5	1.0041	0.3893	−0.0002	0.0003
αj7	0.1514	0.0617	2.4554	0.0912	−0.0448	0.3476
αj8	−1.7427	1.6106	−1.0820	0.3585	−6.8684	3.3830
αj9	1.2270	0.3721	3.2972	0.0458	0.0427	2.4114

,

Table 6. Dependence of α_j3 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9953
R-square	0.9906
Adjusted R-square	0.9655
Standard error	0.0188
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	0.1120	0.0140	39.5032	0.0059
Residual	3	0.0011	0.0004
Total	11	0.1131
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	−0.0057	0.2285	−0.0249	0.9817	−0.7329	0.7215
αj2	−0.0066	0.0033	−1.9701	0.1434	−0.0172	0.0041
αj1	0.0043	0.0016	2.7452	0.0710	−0.0007	0.0092
αj4	−0.0026	0.0066	−0.4023	0.7144	−0.0235	0.0182
αj5	−1.56 × 10−6	2.502 × 10−6	−0.6229	0.5775	−9.5 × 10−6	6.4 × 10−6
αj6	9.034 × 10−7	6.24 × 10−7	1.4477	0.2435	−1.1 × 10−6	2.9 × 10−6
αj7	0.0010	0.0007	1.4435	0.2446	−0.0013	0.0033
αj8	−0.0038	0.0165	−0.2285	0.8339	−0.0563	0.0487
αj9	0.0099	0.0040	2.4681	0.0902	−0.0029	0.0228

,

Table 7. Dependence of α_j4 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9004
R-square	0.8106
Adjusted R-square	0.3057
Standard error	1.6142
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	33.4639	4.1830	1.6054	0.3801
Residual	3	7.8168	2.6056
Total	11	41.2807
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	28.6007	10.5507	2.7108	0.0731	−4.9763	62.1778
αj3	−19.3983	48.2202	−0.4023	0.7144	−172.8565	134.0598
αj2	0.1404	0.4263	0.3293	0.7636	−1.2162	1.4970
αj1	0.1223	0.2398	0.5099	0.6453	−0.6409	0.8854
αj5	−0.0003	0.0002	−1.5413	0.2209	−0.0008	0.0003
αj6	4.886 × 10−5	6.378 × 10−5	0.7662	0.4994	−0.0002	0.0003
αj7	−0.0591	0.0728	−0.8117	0.4764	−0.2908	0.1726
αj8	0.9102	1.3270	0.6859	0.5420	−3.3129	5.1332
αj9	−0.2588	0.5826	−0.4442	0.6870	−2.1127	1.5952

,

Table 8. Dependence of α_j5 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9518
R-square	0.9060
Adjusted R-square	0.6552
Standard error	4087.2041
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	482,815,236	60,351,904.5060	3.6128	0.1593
Residual	3	50,115,711.85	16,705,237.2820
Total	11	532,930,947.9
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	62,239.5686	34,213.8745	1.8191	0.1665	−46,644.2500	171,123.3871
αj4	−1683.2465	1092.0910	−1.5413	0.2209	−5158.7675	1792.2745
αj3	−73,475.8448	117,949.5670	−0.6229	0.5775	−448,844.0085	301,892.3189
αj2	−76.4352	1097.8010	−0.0696	0.9489	−3570.1280	3417.2575
αj1	579.3524	537.3703	1.0781	0.3599	−1130.7999	2289.5046
αj6	0.2179	0.1239	1.7589	0.1768	−0.1764	0.6122
αj7	−109.7099	193.4607	−0.5671	0.6103	−725.3883	505.9685
αj8	1350.4587	3528.7569	0.3827	0.7274	−9879.6206	12,580.5379
αj9	−292.2135	1513.4400	−0.1931	0.8592	−5108.6551	4524.2282

,

Table 9. Dependence of α_j6 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9514
R-square	0.9052
Adjusted R-square	0.6525
Standard error	13,363.9686
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	5,116,934,684	639,616,835.5	3.5814	0.1610
Residual	3	535,786,970.9	178,595,657
Total	11	5,652,721,655
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	−112,988.1653	148,539.9796	−0.7607	0.5022	−585,708.6746	359,732.3441
αj5	2.3297	1.3246	1.7589	0.1768	−1.8856	6.5451
αj4	3349.1357	4371.3674	0.7662	0.4994	−10,562.5064	17,260.7779
αj3	455,245.6501	314,468.6491	1.4477	0.2435	−545,533.9402	1,456,025.2404
αj2	3120.5658	3107.9385	1.0041	0.3893	−6770.2815	13,011.4132
αj1	−2694.5364	1364.9806	−1.9740	0.1429	−7038.5140	1649.4412
αj7	−110.9712	662.5111	−0.1675	0.8776	−2219.3772	1997.4347
αj8	1143.0401	11,797.8525	0.0969	0.9289	−36,402.9920	38,689.0722
αj9	−3029.7834	4661.7779	−0.6499	0.5621	−17,865.6412	11,806.0743

,

Table 10. Dependence of α_j7 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9771
R-square	0.9546
Adjusted R-square	0.8337
Standard error	11.5921
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	8485.1905	1060.6488	7.8932	0.0584
Residual	3	403.1270	134.3757
Total	11	8888.3175
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	157.0741	107.6048	1.4597	0.2405	−185.3723	499.5206
αj6	−8.34949 × 10−5	0.0005	−0.1675	0.8776	−0.0017	0.0015
αj5	−0.0009	0.0016	−0.5671	0.6103	−0.0058	0.0041
αj4	−3.0474	3.7544	−0.8117	0.4764	−14.9954	8.9007
αj3	394.2114	273.0981	1.4435	0.2446	−474.9087	1263.3315
αj2	4.4103	1.7962	2.4554	0.0912	−1.3060	10.1266
αj1	−1.6153	1.5340	−1.0531	0.3696	−6.4971	3.2664
αj8	9.0491	8.8181	1.0262	0.3803	−19.0140	37.1122
αj9	−6.4419	2.1957	−2.9339	0.0608	−13.4295	0.5458

,

Table 11. Dependence of α_j8 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9442
R-square	0.8916
Adjusted R-square	0.6025
Standard error	0.6530
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	10.5186	1.3148	3.0838	0.1920
Residual	3	1.2791	0.4264
Total	11	11.7977
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	−7.8771	6.4924	−1.2133	0.3118	−28.5388	12.7845
αj7	0.0287	0.0280	1.0262	0.3803	−0.0603	0.1178
αj6	0.0000	0.0000	0.0969	0.9289	−0.0001	9.23644 × 10−5
αj5	0.0000	0.0001	0.3827	0.7274	−0.0003	0.0003
αj4	0.1489	0.2171	0.6859	0.5420	−0.5421	0.8400
αj3	−4.5366	19.8532	−0.2285	0.8339	−67.7184	58.6453
αj2	−0.1611	0.1489	−1.0820	0.3585	−0.6348	0.3127
αj1	0.0537	0.0963	0.5576	0.6160	−0.2527	0.3600
αj9	0.2235	0.2062	1.0836	0.3579	−0.4329	0.8799

and

Table 12. Dependence of α_j9 on other variables based on factual data on G7 and BRICS in 2021.

Regression statistics
Multiple R	0.9934
R-square	0.9869
Adjusted R-square	0.9521
Standard error	1.5496
Observations	12
Analysis of variance
	df	SS	MS	F	Significance F
Regression	8	544.4388	68.0549	28.3414	0.0096
Residual	3	7.2038	2.4013
Total	11	551.6426
	Coefficients	Standard error	t-Stat	p-value	Lower 95%	Upper 95%
Y-intercept	18.2335	15.5901	1.1696	0.3266	−31.3812	67.8481
αj8	1.2586	1.1616	1.0836	0.3579	−2.4380	4.9552
αj7	−0.1151	0.0392	−2.9339	0.0608	−0.2400	0.0098
αj6	−4.0736 × 10−5	6.26784 × 10−5	−0.6499	0.5621	−0.0002	0.0002
αj5	−4.20035 × 10−5	0.0002	−0.1931	0.8592	−0.0007	0.0007
αj4	−0.2385	0.5369	−0.4442	0.6870	−1.9471	1.4701
αj3	67.3765	27.2990	2.4681	0.0902	−19.5009	154.2540
αj2	0.6387	0.1937	3.2972	0.0458	0.0222	1.2552
αj1	−0.2867	0.1738	−1.6495	0.1976	−0.8397	0.2664

affirm the quality and reliability of each equation from system (3). This comprehensive analysis enhances our understanding of the models’ robustness and underscores their significance within the context of our research.

The regression statistics presented in Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, Table 10, Table 11 and Table 12 confirmed the reliability of the system of Equation (3), as all multiple regression coefficients exceed 0.90. To assess the proprietary methodology’s practical value, we will compare the actual statistics for 2021 with the forecast for that year (

Table 13. Statistics of the G7 and BRICS in 2021: factual data and authors’ forecasts.

Category of Countries	Country	Digital Competitiveness Index, Score 1–100		Innovation Index, Score 1–100		Human Development Index, Share of 1		Economic Growth Rate, %		GDP, Billion Dollars		GDP per Capita, Dollars		Quality of Life Index, Score 1–200		Happiness Index, Score 1–10		Sustainability Index, Score 1–100
		αj1		αj2		αj3		αj4		αj5		αj6		αj7		αj8		αj9
		2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021	2021 Factual Data	Forecast for 2021
Major Advanced Economies—G7 (developed countries)	Canada	87.31	95.43	53.10	56.61	0.93	0.97	5.69	1.90	2015.98	1771.42	52,791.23	47,230.92	157.25	178.00	7.10	7.65	79.16	81.84
	France	75.66	86.70	55.00	57.00	0.90	0.94	6.29	1.80	2940.43	2639.72	45,028.27	40,303.60	153.60	164.00	6.69	6.93	81.47	85.63
	Germany	79.33	90.58	57.30	61.14	0.95	0.99	3.05	1.46	4230.17	3726.41	50,787.86	44,683.83	175.24	193.63	7.16	7.34	82.48	85.21
	Italy	61.77	71.34	45.70	48.64	0.89	0.93	5.77	0.82	2120.23	1936.22	35,584.88	31,876.96	137.77	151.09	6.48	6.54	78.76	79.64
	Japan	73.01	86.97	54.50	57.45	0.92	0.96	2.36	0.87	5103.11	5239.46	40,704.30	41,767.44	164.06	185.39	5.94	6.18	79.85	82.89
	UK	85.83	93.18	59.80	64.40	0.93	0.97	6.76	1.65	3108.42	2686.67	46,200.26	40,142.87	156.94	175.17	7.06	7.41	79.97	83.42
	USA	100.00	102.50	61.30	64.86	0.93	0.97	5.97	2.19	22,939.58	21,881.26	69,375.38	66,153.36	163.60	185.72	6.95	7.24	76.01	78.27
BRICS (developing countries)	Brazil	51.48	59.66	34.20	35.19	0.77	0.79	5.23	2.05	1645.84	2459.35	7741.15	11,673.44	104.70	108.07	6.33	6.55	71.34	73.45
	China	84.43	87.70	54.80	57.03	0.76	0.79	8.02	6.30	16,862.98	14,564.78	11,891.20	10,349.69	103.16	103.90	5.34	5.40	72.06	76.16
	India	55.13	67.58	36.40	38.06	0.65	0.67	9.50	8.19	2946.06	3109.50	2116.44	2283.53	103.00	120.07	3.82	4.18	60.07	63.57
	Russia	60.27	73.25	36.60	39.14	0.82	0.86	4.69	1.58	1647.57	1737.83	11,273.24	12,144.00	97.91	108.25	5.48	5.88	73.75	73.76
	South Africa	43.64	63.32	32.70	35.42	0.71	0.73	5.00	2.31	415.31	357.05	6861.17	6083.80	131.37	141.23	4.96	4.91	63.74	63.98

) and perform a plan-fact analysis (

Table 14. Plan-fact analysis of global economic indicators for G7 and BRICS in 2021.

Category of Countries	Country	Digital Competitiveness Index, Score 1–100	Innovation Index, Score 1–100	Human Development Index, Share of 1	Economic Growth Rate, %	GDP, Billion Dollars	GDP per Capita, Dollars	Quality of Life Index, Score 1–200	Happiness Index, Score 1–10	Sustainability Index, Score 1–100
Major Advanced Economies—G7 (developed countries)	Canada	−8.51	−6.20	−4.23	199.37	13.81	11.77	−11.66	−7.15	−3.27
	France	−12.74	−3.51	−4.15	249.61	11.39	11.72	−6.34	−3.46	−4.86
	Germany	−12.42	−6.28	−4.34	108.97	13.52	13.66	−9.50	−2.52	−3.20
	Italy	−13.42	−6.04	−4.09	603.66	9.50	11.63	−8.82	−0.87	−1.10
	Japan	−16.05	−5.13	−4.27	171.03	−2.60	−2.55	−11.51	−3.88	−3.67
	UK	−7.89	−7.14	−3.92	309.88	15.70	15.09	−10.41	−4.67	−4.14
	USA	−2.44	−5.49	−4.54	172.74	4.84	4.87	−11.91	−3.99	−2.89
BRICS (developing countries)	Brazil	−13.71	−2.81	−3.16	155.07	−33.08	−33.69	−3.12	−3.36	−2.87
	China	−3.73	−3.91	−3.67	27.22	15.78	14.89	−0.71	−1.13	−5.38
	India	−18.43	−4.36	−3.73	15.96	−5.26	−7.32	−14.22	−8.64	−5.51
	Russia	−17.72	−6.49	−4.19	196.84	−5.19	−7.17	−9.55	−6.85	−0.01
	South Africa	−31.08	−7.68	−2.88	116.45	16.32	12.78	−6.98	0.94	−0.38
The average for the G7 countries		−10.49	−5.69	−4.22	259.32	9.45	9.46	−10.02	−3.79	−3.30
The average for the BRICS countries		−16.93	−5.05	−3.53	102.31	−2.29	−4.10	−6.92	−3.81	−2.83

).

The Digital Competitiveness Index in 2021 was below the predicted value by 10.49% in the G7 countries and by 16.93% in the BRICS countries. The Innovation Index in 2021 was below the expected value by 5.69% in the G7 countries and by 5.05% in BRICS countries (Table 4). The human development index 2021 was below the predicted value of 4.22% in the G7 countries and 3.53% in the BRICS countries. This could be explained by the reduced rate of innovation and digital development in society and the economic impact of the COVID-19 pandemic and crisis.

On the contrary, economic growth was more rapid than the predicted value: 259.32% in the G7 and 102.31% in BRICS. GDP in the G7 exceeded the forecast by 9.45%, below the expected value of 2.29% in BRICS. GDP per capita in the G7 exceeded the estimates by 9.46% and was lower than the predicted value by 4.10% in BRICS.

The quality-of-life index 2021 was below the predicted value of 10.02% in G7 countries and 6.92% in BRICS countries. The Happiness Index in 2021 was below the expected value by 3.79% in G7 countries and by 3.81% in BRICS countries. The Sustainability Index in 2021 was below the predicted value by 3.30% in the G7 and by 2.83% in BRICS countries.

The indicators exceeded the projected values by 26.75% in G7 nations and 6.32% in BRICS nations. This variation illustrates a minor discrepancy between the actual data and the forecasts, emphasizing their accuracy and reliability.

This instance underscores the practical significance of the data as follows:

-

The dataset enables swift compilation of a diverse array of information with just a few clicks, facilitating the selection process and promoting enhanced decision-making;

-

The dataset offers valuable insights into the G7 and BRICS countries. By pinpointing the distinct factors that influence sustainable development within these nations, customized recommendations can be formulated to optimize the efficacy of economic management and foster sustainable growth.

A synergistic effect exists in how countries adapt to climate change, characterized by the interconnectedness and mutual reinforcement of processes associated with Industry 4.0, the knowledge economy, economic growth, and sustainable development. The magnitude of this effect varies among countries with differing income levels, as demonstrated by examples drawn from the G7 and BRICS nations.

5. Discussion

This paper contributes to the advancement of theories about datasets, big data, and open data by delineating their potential practical applications (

Table 15. Comparison of New Results with Existing Theory.

	Existing Theory	New Results
Preferred sources for studying the world economy	Available studies on the global economy focus on a limited selection of countries and key indicators	Deficits in statistics can be resolved using big data and open data for comprehensive global economic research
The scale of datasets, big data, and open data	Datasets should be specialized, reflecting only the specific aspects of the world economy	Datasets must contain detailed statistics on the set topic reflecting the indications of the world economy and its factors
Technologies of socioeconomic forecasts	Forecasts should be grounded in the experiences of specific countries and tailored to each national economy	Predictions should be grounded in global economic experience
Approach to analyzing economic system crises through statistics	Emphasize the phenomenon of economic crises by concentrating on GDP and growth rates	Examining how crises impact quality of life and sustainable development, along with strategies for addressing these challenges

). Likewise, it illustrates how financial stability was preserved during the COVID-19 crisis through the effective use of these data modalities. The findings align with existing literature on financial risk management during crises, such as those addressed by Battaglia et al. (2024), Cardillo and Chiappini (2022), Desogus et al. (2024), Ergasheva et al. (2023), Fiorillo et al. (2024), Kantor et al. (2023), Maharana et al. (2025), Piserà and Chiappini (2024), Setiawan and Septiani (2025), and Zedda et al. (2021). This stream of literature primarily focuses on non-economic crises, including pandemics and climate events.

Contrary to Bibri (2018) and Burkart et al. (2016), who advocate for using fragmented statistical reports, our findings suggest that strategically utilizing datasets, big data, and open data can effectively address deficiencies and inconsistencies within statistical reporting. Furthermore, while Chen et al. (2020) and Emmanuel et al. (2021) promote a narrow thematic focus in constructing datasets, this paper asserts that these datasets should encompass a comprehensive range of statistics capable of capturing various dimensions of the global economy and their contributing factors.

In contrast to Batko and Ślęzak (2022), Bedianashvili (2021), and Benati and Coccia (2022), who support the development of forecasts predicated solely on the distinct experiences of individual nations, this paper contends that such forecasts ought to consider the broader context of the global economy. Finally, while Ang and Seng (2016) and Håkansson (2021) advocate for the inclusion of solely economic indicators in statistical analyses, this paper emphasizes the necessity for statistics also to reflect crisis phenomena that extend beyond GDP and economic growth rates, focusing specifically on their implications for quality of life and sustainable development.

Building on the findings of Feng et al. (2024), Fertő and Harangozó (2025), Horvey and Odei-Mensah (2025), and Xie et al. (2024), our results affirm that Industry 4.0, the knowledge economy, economic growth, and sustainable development are closely interconnected. This interconnection creates a synergetic effect on countries’ climate adaptation, which varies according to income categories. Moreover, the results show that dataset analytics is the most effective in reducing uncertainty during crises among various forecasting methods. This aligns with the findings of Lee et al. (2025) and Yhang et al. (2025). Dataset analytics is significantly preferred over scenario analysis, as highlighted by Berti et al. (2025) and Jihan et al. (2025). It offers a more reliable forecasting approach compared to the “other conditions being equal” method discussed by Guan et al. (2025) and Mislan and Dani (2025). Additionally, dataset analytics proves more effective than foresight methods, Martin et al. (2025) and Wach et al. (2025) noted.

6. Conclusions

This dataset offers a fresh perspective on analyzing the global economy by consolidating all relevant information into a user-friendly platform. It organizes statistics chronologically by indicators and countries, which makes it easier to delve into important issues such as state budget deficits, rising government debt, and the impacts of the COVID-19 pandemic. Additionally, the dataset includes forecasts for economic growth through 2024, allowing for comparisons between projected and actual performance.

The robust methodology behind the dataset’s analytics closely links the implications for national economic strategies, enhancing the tools available for climate management within modern economic systems. The proposed digital platform aims to support socioeconomic forecasting related to climate risks and financial management in both developed and developing countries.

Implementing such innovative digital platforms could greatly improve the accuracy of socioeconomic forecasts, offering critical insights for governmental and corporate planning. Moreover, this methodology facilitates an evaluation of countries’ resilience, which helps strengthen management strategies for climate adaptation. This is especially vital, as these challenges often intertwine with economic crises, which the approach outlined in this paper can help anticipate. Furthermore, further research is recommended to pinpoint the key factors that drive recovery, particularly in areas like healthcare investments, to better manage the financial risks posed by climate change.