This study aims to analyze the effect of fiscal policy represented by government expenditure together with inflation on economic growth in Indonesia during the 2007–2021 period. A quantitative approach was employed using the Ordinary Least Squares (OLS) method with EViews 12 software. Economic growth was used as the dependent variable, while government expenditure and inflation served as the independent variables. The results indicate that government expenditure and inflation simultaneously exert a significant influence on economic growth, as reflected by the Prob(F-statistic) value of 0.040772. However, the partial test shows that both independent variables have no significant effect on economic growth. Government expenditure exhibits a negative and insignificant relationship, whereas inflation shows a positive yet insignificant influence. These findings imply that the increase in government spending has not been fully directed toward productive sectors, particularly during the COVID-19 pandemic period. In addition, low inflation does not indicate strengthened aggregate demand. Overall, this study emphasizes the importance of synergy between fiscal policy and price stability in supporting sustainable economic growth in Indonesia.

This article explores the theoretical foundations and historical perspectives of fiscal policy, emphasizing its role in economic stabilization and the management of economic cycles. It traces the evolution of economic ideas, from classical orthodoxy, focused on strict budgetary balance and limited state intervention, to the Keynesian revolution, which positioned fiscal policy as a strategic tool for influencing aggregate demand. The article also examines the main instruments of fiscal policy, including public spending, taxation, and debt, while detailing their effectiveness and limitations in various economic contexts. Finally, particular attention is given to automatic stabilizers and fiscal approaches specific to developing countries, highlighting the importance of balanced management to achieve sustainable growth and macroeconomic stability.

This article presents household-level socioeconomic data on food-water-energy nexus consumption collected through a survey conducted during the first quarter of 2020 in the urban areas of the Pune Metropolitan Region, India. The dataset includes 1872 observations from households residing in both formal and informal settlements. Data were collected via door-to-door interviews in the local language using a comprehensive, structured questionnaire administered through a computer-assisted web interviewing mobile application developed by the World Bank. Quality control was ensured through digital data capture, daily monitoring during fieldwork, and post-collection data validation. The dataset comprises 606 variables, including consumption data for water, energy, and food, alongside socioeconomic factors such as household composition, income, housing conditions, migration history, and household-level strategies to cope with intermittent water supply. The dataset can be used for econometric modeling of household demand, parameterization of multi-agent models, comparative analyses across regions, and empirical studies examining household challenges related to water, energy, and food security.

Artificial intelligence (AI) is advancing rapidly and is emerging as a significant driver of global electricity consumption, yet its long-term energy and emissions implications remain poorly quantified. This study develops a scenario-based, simulation-driven modeling framework that links mathematical representations of AI computational demand with life-cycle carbon accounting for global AI-related energy use and emissions through 2050. We evaluate alternative development pathways that differ in model scale, deployment structure, and electricity mix assumptions. Across all scenarios, improvements in hardware and algorithmic efficiency substantially reduce energy use per operation; however, aggregate AI electricity demand still increases by roughly an order of magnitude due to rapid growth in training and inference workloads. Under the continuation of current trends, AI electricity consumption could reach up to 30% of global demand by 2050, corresponding to more than 8 gigatons of annual CO2-equivalent emissions. Even under optimistic efficiency trajectories, total AI-related electricity demand remains more than six times higher than 2024 levels. In contrast, scenarios that combine consolidation toward fewer, larger models with transitions to low-carbon electricity sources reduce total emissions by up to 40% relative to business-as-usual pathways, exceeding the reductions achievable through efficiency gains alone by more than 20 percentage points. These results highlight widening regional disparities and indicate that policy choices affecting AI deployment patterns and electricity system decarbonization play a central role in shaping the carbon intensity of computation.

Purpose This study aims to examine how teleworkers influence Brazil's economic structure. It shifts the focus from organizational adaptation to the systemic effects on employment, income, and sectoral demand, especially in knowledge-intensive services. Design/methodology/approach The study uses input–output (I–O) analysis with 2021 data, combining national accounts with microdata from household surveys (PNAD-C and POF). The study estimates type II multipliers disaggregated by demographics and uses the hypothetical extraction method to simulate the removal of teleworkers' labor supply and consumption from the economy. Findings The results reveal a structural asymmetry: the Brazilian economy is more sensitive to the withdrawal of teleworkers' consumption than to the withdrawal of their labor supply. Although professional services generate strong employment and income multipliers, technology-driven sectors like systems development create limited indirect jobs. Consequently, teleworkers' systemic weight is predominantly driven by their high-income consumption patterns rather than their role in production chains. Originality/value The paper offers a novel dual perspective, viewing teleworkers as both labor inputs and structural vectors of final demand. Through an empirical analysis, it demonstrates how a concentrated workforce alters sectoral dynamics through consumption patterns, thereby mirroring labor market inequalities within the I–O framework.

This paper presents an econometric analysis of inflation from a Post Keynesian perspective using quarterly data from 2002 to 2024 for the United States, including the most recent period of inflation after the onset of the COVID-19 pandemic. I evaluate the continued relevance of the comprehensive Post Keynesian model of inflation using a reduced form equation that incorporates both the aggregate demand–augmented wage-cost markup equation and the wage growth equation which is robust in explaining inflation. The robustness of the model is tested by incorporating different measures of labor market slack, wages, and inflation. The paper finds that the comprehensive model is not robust for the period 2002 to 2024 even when alternative measures for wages, unemployment, and inflation are utilized. This discrepancy arises because the negative relationship between unit labor costs and inflation, observed in the updated model, proved non-robust upon the inclusion of control variables for energy costs and imports. After employing the Prais–Winsten estimation to account for persistent serial correlation, the revised model aligned with the sign conventions of the original Atesoglu wage-cost markup equation. Specifically, while the coefficient for unit labor costs turned positive, it failed to reach statistical significance. Finally, I discuss potential factors for the decrease in the magnitude and significance of the coefficients in the aggregate demand–augmented wage–cost markup model for the period 2002 to 2024 as well as the pass through of wage growth to broader inflation measures.

Climate change mitigation in Thailand requires urgent transformation of energy-intensive sectors, notably transport in rapidly urbanizing provinces. Khon Kaen, a central economic hub in northeastern Thailand, faces increasing energy demand and transport-related greenhouse gas (GHG) emissions driven by rising private vehicle ownership and limited public transit integration. This study applies the Low Emissions Analysis Platform (LEAP) to model long-term energy demand and GHG emissions under two scenarios: Business-as-Usual (BAU) and Low-Carbon Scenario (LCS). A bottom-up vehicle stock turnover approach was combined with socioeconomic projections to simulate transport energy consumption from 2024 to 2050. The LCS integrates electric vehicle (EV) promotion, expansion of Light Rail Transit (LRT), Double-Track Rail (DTR) and High-Speed Rail (HSR), and implementation of Transit-Oriented Development (TOD) strategies. Results show that, compared with BAU, the LCS can reduce transport-related GHG emissions by 62.9% by 2050 and final energy demand by 43.5%, reflecting a substantial shift from fossil fuels toward electricity and biofuels. Under the LCS, adoption of EVs is projected to reach 100% of new passenger car sales by 2050, supported by the electrification of rail transport and decreased Vehicle Kilometres Travelled through TOD-based planning. These findings confirm that locally calibrated, integrated transport and land-use measures can significantly support Thailand’s national targets for carbon neutrality by 2050 and net-zero emissions by 2065. The modelling framework may potentially transferable to other mid-sized cities and provides evidence-based guidance for low-carbon urban transport planning.

Climate change mitigation in Thailand requires urgent transformation of energy-intensive sectors, notably transport in rapidly urbanizing provinces. Khon Kaen, a central economic hub in northeastern Thailand, faces increasing energy demand and transport-related greenhouse gas (GHG) emissions driven by rising private vehicle ownership and limited public transit integration. This study applies the Low Emissions Analysis Platform (LEAP) to model long-term energy demand and GHG emissions under two scenarios: Business-as-Usual (BAU) and Low-Carbon Scenario (LCS). A bottom-up vehicle stock turnover approach was combined with socioeconomic projections to simulate transport energy consumption from 2024 to 2050. The LCS integrates electric vehicle (EV) promotion, expansion of Light Rail Transit (LRT), Double-Track Rail (DTR) and High-Speed Rail (HSR), and implementation of Transit-Oriented Development (TOD) strategies. Results show that, compared with BAU, the LCS can reduce transport-related GHG emissions by 62.9% by 2050 and final energy demand by 43.5%, reflecting a substantial shift from fossil fuels toward electricity and biofuels. Under the LCS, adoption of EVs is projected to reach 100% of new passenger car sales by 2050, supported by the electrification of rail transport and decreased Vehicle Kilometres Travelled through TOD-based planning. These findings confirm that locally calibrated, integrated transport and land-use measures can significantly support Thailand’s national targets for carbon neutrality by 2050 and net-zero emissions by 2065. The modelling framework may potentially transferable to other mid-sized cities and provides evidence-based guidance for low-carbon urban transport planning.

ABSTRACT This paper considers Keynes’ insights from The Economic Consequences of Mr. Churchill (ECMC) in light of three recent events: the onset of the COVID-19 Pandemic, the 2022 pivot toward inflation targeting monetary policy, and the economic policies enacted during the second presidential term of Donald Trump. It argues that Keynes’ insight that governments have the capacity to derail economic recoveries through an array of policies that hurt workers and households can help us understand the policy successes and failures of these three events. At the time of writing, Keynes’ policy insights offer a blueprint for future recovery minded policies: strong economies should provide ample fiscal and monetary assistance to promote domestic aggregate demand, while eschewing policies that disproportionately burden households and workers.

This paper examines the mechanisms by which uncertainty influences economic activity. It describes the channels through which uncertainty exerts both a negative influence – the real options effect, the financial channel, and reduced consumer spending – and a positive influence—growth options and the Oi-Hartman-Abel effect. Aspects of the real options mechanism, such as companies' wait-and-see behavior and the sunk costs associated with real investment and hiring, necessitate adjustments to the traditional approach to evaluating investment projects. In addition to the negative impact of uncertainty on investment, hiring, and employment, the real options channel can lead to a lower sensitivity of economic agents to changes in the economic environment, and, consequently, to lower economic policy effectiveness. The financial channel of uncertainty is associated with financial frictions and increased financing costs due to an increase in the risk premium, which amplify the real impact of uncertainty shocks. This amplifying effect can be quantified using the financial uncertainty multiplier. A decline in consumer spending has a negative impact on aggregate demand in the short term and, if prices and interest rates are insufficiently flexible, may lead to a decline in aggregate output. On the other hand, according to the growth options mechanism, high uncertainty can stimulate increased investment, particularly in technology- and capital-intensive companies, since uncertainty increases the size of potential gains. Also, under the Oi-Hartman-Abel effect, in the case of a firm's profit being convexly dependent on demand or costs, an increase in uncertainty regarding these variables can lead to an increase in expected benefits, which creates incentives for expansionary behavior. The main difficulty in the interaction between the negative and positive effects of uncertainty is determining the significance of each individual transmission mechanism. A review of studies determining the prevailing effect is provided, the majority of which confirm the negative aggregate impact of uncertainty shocks.

Aggregate demand

Abstract This study investigates the distributive effects of carbon taxation on household consumption in Spain, with a particular focus on regional and demographic disparities. By applying a multiregional input–output model across Spain’s seventeen autonomous communities and incorporating household heterogeneity, the paper evaluates the regressivity of a carbon tax imposed on all emissions embodied in final household demand. The analysis simulates a carbon tax of €100 per ton of CO₂, assuming full pass-through to consumer prices, resulting in an average increase of 3.9% in household expenditure. The findings confirm the regressive nature of the tax, as households in lower-expenditure and less densely populated regions experience a disproportionately higher burden. Moreover, the study reveals demographic variations: younger households are more affected by transport-related taxes, while older households face greater impacts from housing-related emissions, regardless of household composition.To address these inequalities, the paper explores various compensation mechanisms. It demonstrates that while incorporating social criteria—such as household structure or age—can reduce regressivity, these measures alone are insufficient. A more equitable outcome requires the integration of economic variables into the design of compensation schemes. The study concludes that public acceptance of carbon taxation hinges on the implementation of well-targeted, transparent, and economically informed redistribution policies. These findings contribute to the broader discourse on climate policy by highlighting the importance of balancing environmental objectives with social equity considerations in the design of fiscal instruments.

Plaza Banten, an MSME marketplace in Banten Province, generates ordering and sales transaction data that can be leveraged to support operational decisions, particularly inventory planning and promotional timing. However, decision-making is often reactive because demand forecasting has not been systematically developed from historical transactions. This study proposes an end-to-end pipeline that transforms Plaza Banten transaction records into daily demand time-series data at the product-category (Group) level, following data preparation and modeling stages in a data mining framework. The study uses transaction data from January to December 2024 and is positioned as a continuation of a previous Market Basket Analysis (MBA) study, which indicated that high transaction volumes were dominated by packaged rice products (e.g., rice boxes and chicken rice packages), motivating a forecasting follow-up for high-demand categories with recurring purchase patterns. The preprocessing stage includes data cleaning, validation of quantity and unit price, feature construction (quantity and revenue), daily demand aggregation by category, and completion of missing calendar dates to form continuous time series. For modeling, this study compares baseline forecasting methods (Naïve and 7-day Moving Average) against an Exponential Smoothing (Holt–Winters/ETS) model that accounts for trend and weekly seasonality. Model performance is evaluated using MAE, RMSE, and MAPE to ensure measurable selection of the best approach. The forecasting results are then interpreted as operational insights to estimate demand levels per category and support inventory planning and promotional prioritization based on predicted demand trends.

Abstract Global cement production reached approximately 4.1 billion metric tons in 2023, while global plastic production climbed to 413.8 million metric tons, projected to reach 590 million metric tons by 2050. This growth underscores the need for sustainable construction materials that can simultaneously divert plastic waste from landfills and reduce demand for virgin aggregates. Controlled Low-Strength Material (CLSM), or flowable fill, offers a promising platform by incorporating large volumes of industrial by-products. This study investigates the use of Recycled Plastic Coarse Aggregates (RPCA) produced from High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), PP (Polypropylene) and mixed plastic waste via a semi-mechanized process as a full replacement for natural coarse aggregates in CLSM. Low-, medium- and high-strength mixes were prepared with Portland cement, fly ash, M-sand and pond ash, and their fresh, hardened and in-service properties were evaluated. Results showed that RPCA-based CLSM achieved flow values of 590–620 mm, wet density reductions of ~ 30%, compressive strength up to 13.4 MPa at 28 days span both excavatable (≤ 8.3 MPa) and structural fill (> 8.3 MPa) CLSM categories as per ASTM D6103, with all low- and medium-strength mixes meeting typical excavatability requirements, shrinkage as low as 0.032%, permeability reductions of 9–15%, and thermal conductivity reductions of ~ 90% leading to a 92% increase in thermal resistivity relative to natural aggregate mixes. An integrated machine learning approach was employed to predict compressive strength from 252 experimental data points using Decision Tree, Random Forest and XG Boost regressors. XG Boost achieved the best performance with R 2 =0.97, MSE 0.08 and MAE = 0.12, outperforming the other models. SHAP analysis revealed that curing age and pond ash content were the most influential variables, followed by fine aggregate and RPCA proportion. This combined experimental–computational framework demonstrates that RPCA-based CLSM can deliver measurable environmental and performance gains while enabling data-driven mix optimisation for sustainable infrastructure applications. Overall, the proposed RPCA-based CLSM aligns with the United Nations Sustainable Development Goals by promoting responsible consumption and production (SDG 12), fostering industry innovation and resilient infrastructure (SDG 9), supporting sustainable cities and communities (SDG 11), and contributing to climate action through material efficiency and reduced embodied energy (SDG 13).

The article analyses the main theories and factors that determine wage stickiness (rigidity) in conditions of macroeconomic instability. The study found that wage rigidity was most widely considered by two economic schools: the non-Keynesian and monetarist schools. They developed the most models and theories. Neo-Keynesians proposed the NANRUE model (non-automatic wage equilibrium with natural unemployment), which explains the procyclicality of wages and reflects the relationship between aggregate demand, unemployment and price rigidity. Monetarists, led by M. Friedman, developed the «breakdown» model, which shows the relationship between macroeconomic shocks and changes in wage levels. However, contemporary authors suggest using many more factors when formulating public policy in the labour market or analysing wage stickiness. These include the minimum wage, strikes, the frequency of wage changes and the structure of jobs, as well as indicators of wage «freezing» and «reduction» as indicators of wage flexibility. The main idea behind the development of modern theories of wage stickiness is the need to introduce micro-indicators and indicators into macro-analysis, which will enrich and make economic models more flexible and accurate. As a result of our analysis, we found that nominal wage rigidity is not a constant phenomenon. It is influenced by both macroeconomic and microeconomic processes and factors. Modern research confirms the asymmetric nature of this rigidity. A positive economic shock has a disproportionate effect on the growth rate. Similarly, a negative shock cannot adequately reduce the level of income. This is because a ‘stickiness’ mechanism is triggered, which manifests itself in rising unemployment, highlighting the role of state fiscal and regulatory intervention. At the same time, it has been found that non-Keynesian models and theories derived from the «breakdown» model have limited predictive power due to their disregard for structural changes in employment and measurement errors. The transition to microdata analysis, the frequency of changes in individual wages, and the consideration of non-economic factors (trade unions and strikes), such as «grey» wages and the statutory minimum wage, allow for a more accurate identification of the degree of labour market adaptability to macroeconomic fluctuations.

Data scarcity makes subnational input-output accounts inaccurate. Builders of such accounts must resort to allocating output, value added, imports, and exports to regions using readily available industry-wise data, like shares of national jobs by industry. Meanwhile, population shares are typically used to allocate other final demand components. Lately, however, some statistical agencies have been releasing more subnational data. Surprisingly, builders of subnational input-output accounts do not appear to use them. This is probably due to uncertain trade-offs between the costs and benefits of deploying such data. We, therefore, explore the degree to which using some value-added and household consumption data can improve subnational multiregional input-output accounts. We find that integrating either household-consumption or some value-added data improves account accuracy little. Using both datasets in combination, however, does improve estimated accounts somewhat. Plus, together they portray rather accurate estimates of interregional income multipliers and consumption-based greenhouse gas emissions.

This study empirically examines how both personal and functional income distribution affect aggregate demand across EU-15 countries over the period 1980–2020. Using an extended post-Kaleckian Bhaduri-Marglin framework, we integrate the strengths of structural and aggregative approaches, estimating each demand component within a system that accounts for endogeneity and cross-equation dependencies. A key contribution is the explicit incorporation of personal income inequality—measured via both the Gini and Theil indices—into a framework traditionally centered on functional distribution. Our findings indicate that aggregate demand cannot be unambiguously classified as either wage-led or profit-led with respect to functional distribution. However, personal income distribution is clearly associated with an “equality-led” demand regime: higher inequality dampens domestic demand. These results carry important policy implications, especially in challenging the widely held notion of a tradeoff between growth and equality.

We adapt the dynamic disequilibrium input-output model of Pichler et al. (2022). [Journal of Economic Dynamics and Control, 144, 104527.] to the Belgian economy and conduct a cross-context validation of the COVID-19 pandemic. Labor supply and export demand shocks are refined using business surveys and observed trade flows, while household demand shocks are calibrated to 115 time series on GDP, revenue, employment, and interindustry transactions. The refined shocks improve the model’s ability to reproduce the observed evolution of GDP, revenue, and employment. However, the model systematically underestimates the persistence of interindustry trade, suggesting structural limitations in its ability to represent firms’ incentives to sustain trade. Relaxing the Leontief production function based on input criticality improves the model’s accuracy, consistent with the original model, though differentiation across degrees of relaxation proved unidentifiable despite a larger dataset. Overall, our results confirm the original model’s validity for assessing epidemic-driven economic impacts, thereby strengthening its credibility as a policy tool.

Rapid growth of water conservancy/hydropower projects has spurred rising demand for sand-gravel aggregates. Under strict water use and zero-waste policies, treating wet-process aggregate washing wastewater is challenging. Flocculants—key chemicals in this process—directly influence treatment efficiency and operational costs via their type, dosage, and efficacy. Further development of the intelligent control system for flocculant dosing can reduce flocculant consumption by 50% to 67%. However, existing studies have an insufficient understanding of the identification of emerging contaminants in aggregate washing wastewater and the migration of flocculants in multi-medium environments, as well as a lack of research on the synergistic effects of multiple flocculants. Another key core challenge lies in the accurate identification of the impact of flocculant residues on concrete performance, along with the problems of high cost and poor adaptability of intelligent systems. Future research directions will focus on precise flocculation, residue control and resource utilization to drive the development of efficient and environmentally friendly treatment technologies.

ABSTRACT In this paper, we study a two-class (‘capitalists’ and ‘workers’) model of growth, distribution, and employment that blends together some recent literature in the classical-Marxian tradition [Franke, R. 2020. ‘An Attempt at the Reconciliation of the Sraffian and Kaleckian View on Desired Utilization.’ European Journal of Economics and Economic Policies: Intervention 17 (1): 61–77; Petach, L., and D. Tavani. 2019. ‘No One is Alone: Strategic Complementarities, Capacity Utilization, Growth and Distribution.’ Structural Change and Economic Dynamics 50: 203–215; Petach, L., and D. Tavani. 2022. ‘Aggregate Demand Externalities, Income Distribution, and Wealth Inequality.’ Structural Change and Economic Dynamics 60: 433–446; Tavani, D., and L. Petach. 2021. ‘Firm Beliefs and Long-Run Demand Effects in a Labor-Constrained Model of Growth and Distribution.’ Journal of Evolutionary Economics 31: 333–357.]. The central feature is the treatment of aggregate demand as a positive externality for individual firms arising from the economy-wide utilization rate. Despite assuming competitive markets, optimizing behavior, and perfect foresight, we show that laissez-faire involves under-utilization of the economy's productive capacity. Moreover, both the long-run labor share of income and workers' share of wealth would be higher at full capacity. Thus, fiscal policy aimed at achieving full utilization are unambiguously worker-friendly. Given the reduction in relative standing both in terms of wealth and income, however, capitalists may oppose such policies. As such, the model formalizes Michal Kalecki's arguments on the political aspects of full employment. Political Quarterly 14 (4): 322–330.] on the political aspects of full employment.