Agriculture, forestry, and other land use contribute about a fifth of total anthropogenic greenhouse gas (GHG) emissions. Mitigation efforts have emphasized “decoupling” that sustains production while lowering emissions per unit of output. However, the underlying decoupling mechanisms have not been fully characterized. We rely on a mathematical identity to decompose agricultural GHG emission growth () into three parts: output (), emissions per unit of input (), and output per unit of input () or total factor productivity (TFP). We then rely on official country-level data to quantify the historical contribution of these components. Over 1961 to 2021, we find that TFP growth—which captures the sector’s ability to produce more output per unit of measured input—has consistently remained one of the main sources of GHG emission reduction within farms. Further decomposition reveals a key role for rising land productivity in reducing emission intensity.

ABSTRACT Global industrial productivity and technological advancement are central to sustaining long-term economic growth and shaping international competitiveness. However, most existing studies emphasize total factor productivity (TFP) growth rather than sectoral TFP differences across major economies. Thus, this study measures and compares TFP across China, the United States, and the European Union in 27 industries from 1995 to 2017. The analysis applies the KLEMS framework in conjunction with the Törnqvist index to evaluate technological and efficiency variations among sectors and economies. Results indicate that the European Union maintains continuous leadership in sectors such as agriculture, basic metals, and industrial machinery, while the United States leads in food manufacturing, utilities, and trade. China shows a comparative advantage in financial intermediations. Several industries exhibit alternating leadership, reflecting dynamic global productivity competition. These findings reveal distinct technological and efficiency advantages across economies. The study recommends that industries with lower TFP should attract capital and advanced technology to enhance innovation and productivity, while high-performing industries should sustain their efficiency advantages through continuous innovation and strategic capital reallocation.

This paper explores the potential macroeconomic impacts of artificial intelligence (AI), focusing particularly on its role in task automation and labor market complementarity. By constructing a task-based economic model, this study examines how AI affects total factor productivity (TFP) and GDP growth, with a particular focus on its distributive effects across different industries and demographic groups. A model of the distributive effects of AI applications is also constructed, with a focus on the manufacturing and service sectors.

Technological innovations are reshaping today’s world, and digitalization has become a key driver influencing national economies. This study investigates the determinants of total factor productivity (TFP) changes across 12 Turkish regions from 2011 to 2023 within the framework of Digital Divide Theory. Employing the Malmquist Index, it assesses the impact of technological disparities on financial and human capital. Results show that the West Marmara, Mediterranean, and Black Sea regions demonstrated high technical efficiency, while Istanbul and the Aegean regions led in technological progress and TFP growth. Deposit distribution, education levels, and R&D human resources positively affected productivity in developed regions. However, income inequality and R&D expenditure had weaker effects than expected, indicating that these factors alone cannot explain regional TFP gaps. The study highlights efficient R&D use, reduced input waste, and technological transformation as vital for sustainable regional development in Turkey.

This paper investigates the artificial intelligence (AI) productivity paradox using total factor productivity (TFP) from 1890 to 2022 and fractional integration and long-memory econometric methods. We find that total factor productivity gains from AI investments may be delayed and diffuse nonlinearly, following long-memory patterns similar to those of previous technological revolutions, resulting in a paradox of long lags, not a lack of innovation. The average TFP growth rate (0.54%) in the AI era is the lowest of any post-war technological wave, with profoundly contradictory persistence measures from the GPH (d=1.730) and Local Whittle (d=0.133) estimators, reflecting fundamental uncertainty about the actual productivity of AI. We observe that the GPH estimator is consistent with the "J-curve" hypothesis of temporary slowdown before long-term gains. In contrast, the Local Whittle estimator suggests productivity effects that may be fleeting and easily commoditized. Cross-country heterogeneity in AI persistence patterns points to the role of local institutions, policies, and complementary investments in mediating the macroeconomic impact of AI. These results imply that the full productivity benefits of AI may be realized over very long-run horizons, providing policymakers and investors with necessary guidance on the timing and nature of the AI revolution.

Abstract In the current digital economy era, digital technology innovation has become a core factor driving China’s economic development. This paper takes listed companies in China as research samples, identifies companies’ digital patents through text analysis to characterize the level of digital technology innovation, and examines the economic consequences of digital technology innovation from the perspective of total factor productivity. The study finds that digital technology innovation significantly enhances the improvement of total factor productivity and empowers high-quality development of Chinese enterprises. The conclusion remains valid after controlling for endogeneity and conducting robustness tests. Regarding the mechanism, digital technology innovation exerts functions of management empowerment, investment empowerment, operational empowerment, and labor empowerment, helping enterprises reduce internal control costs, improve the quality of investment decisions and asset operation efficiency, and optimize labor resource structure, thereby promoting the growth of total factor productivity. Further analysis reveals that a sound intellectual property protection system and digital infrastructure will amplify the positive impact of digital technology innovation on productivity; meanwhile, for high-tech enterprises and labor-intensive enterprises, the productivity effects of digital technology innovation are more pronounced. This study validates the empowering role of digital technology innovation in high-quality development of the real economy, providing insights for policy support of digital technology innovation and strategic decision-making in enterprise digitalization.

The Indian automobile industry holds a crucial position in the nation's economy, contributing significantly to GDP, employment, and exports. Productivity is a vital driver of competitiveness in this sector. This research focuses on assessing productivity trends within the Indian automobile sector from 1987-88 to 2021-22, applying the methodology of growth accounting approach. Total Factor Productivity Growth (TFPG) has been acknowledged as a detailed measure of the effective use of inputs. The study utilized the Divisia-Tornquist index approach for analysis. Partial productivity indices (PPI) for labour and capital have also been evaluated to evaluate the effectiveness of individual input usage. The outcomes point to a significant reduction in Total Factor Productivity (TFP) observed during the study period, reflecting systemic inefficiencies. While labour productivity (LPI) showed consistent growth, especially during the mid-2000s, capital productivity (KPI) experienced a continuous and significant decline. TFPG exhibited fluctuating trends, with frequent negative growth, particularly during economic downturns. The results underscore the need for improved technological adoption, resource optimization, and policy interventions to enhance productivity and sustain the industry's global competitiveness.

ABSTRACT China’s hog production has undergone a structural transition, from the traditional backyard production mode to the large-scale production mode. However, whether the large-scale producers have comparative advantages in hog production remains unclear. Using provincial level data over four decades, this study shows that total factor productivity of large-scale producers is higher than that of small-scale and backyard producers. That is, ‘the bigger the better’ is evidenced in China’s hog production. Moreover, robustness tests using TFP estimates across multiple livestock species further confirm this scale-productivity relationship, demonstrating China’s livestock sector exhibits a similar scale-TFP pattern to that observed in other developed countries. In addition, this study also shows that technical progress is the major contributor of the growth of total factor productivity.

In light of fiscal constraints and the need to improve resource allocation in the SUS, this article analyzes the effects of the Lean Project in Emergency Care on the technical efficiency and productivity of public hospitals between 2019 and 2023. The DEA-SBM models and the Malmquist Index are applied to secondary data from DATASUS. The results indicate an average increase of 51% in technical efficiency and growth in total factor productivity, driven by innovations. Smaller hospitals, especially in the South and Southeast regions, performed better. Despite the advances, 62.4% of the units still operate below the efficiency frontier. It is estimated that modest efficiency gains could generate savings of more than R$3.5 billion per year, reinforcing the need to adopt management strategies that promote greater efficiency in resource allocation, based on evidence and robust assessment tools.

The dynamic relationship between foreign direct investment (FDI) and sustainable development has become a central topic of inquiry for academics and policymakers with rapid global economic growth. This study aims to clarify the impact mechanism and regional heterogeneity of FDI on total factor productivity (TFP) of Chinese high-tech enterprises, providing empirical evidence for optimizing foreign investment policies and promoting sustainable growth of enterprises. We utilized panel data from 30 provinces in China from 2009 to 2022. The DEA-Malmquist index method is firstly employed to dynamically measure the TFP of high-tech enterprises, while a static panel model is utilized to empirically test the impact of FDI on TFP. A particular emphasis is then placed on analyzing the regional heterogeneity of technology spillovers. The findings reveal that FDI significantly enhances both the production efficiency and the technological innovation capacity of high-tech enterprises overall, thereby facilitating the sustainable growth of enterprises. Furthermore, technological innovation emerges as the core driving force behind TFP growth, whereas the expansion of labor input significantly decreases efficiency improvements. Notably, the technology spillover effects of FDI illustrate significant heterogeneity across different regions and types of enterprises. To promote the sustainable development of high-tech enterprises, this study provides evidence-based insights for foreign direct investment technologies to better enhancing the overall sustainable competitiveness of the economy in China.

This paper provides analytic guides to recent literature on China’s macroeconomic development, emphasizing the critical role of the gradualist reform approach. Our analysis suggests that from 1978 to 1997, the gradualist approach contributed to China’s aggregate total factor productivity and economic growth primarily through policies that facilitated the reallocation of surplus labor from agriculture to nonagricultural sectors. Since 1998, the government’s focus shifted, with various reforms encouraging large enterprises, whether state owned or privately owned, to enter capital-intensive sectors, making capital deepening the main driver of economic growth. While this strategy sustained China’s GDP growth, it also increased trade tensions with global partners, created barriers to transitioning to a consumption-led economy, and threatened China’s long-term financial stability, casting long shadows over the Chinese economy. (JEL E23, F14, L16, O11, O47, P21, P24)

As a new economic form guiding future development, the digital economy is becoming a core engine driving high-quality economic development. Based on the dialectical principle of productivity and production relations, this article systematically analyzes the internal mechanisms of the digital economy in promoting high-quality development and, drawing on my country's development practices, explores practical implementation paths. Research shows that by 2024, the added value of my country's core digital economy industries will account for 10.4% of GDP, and digital technology will contribute 22.5% to total factor productivity growth. This data fully demonstrates the key role of the digital economy in improving economic efficiency, enabling innovation, and optimizing the industrial structure. Currently, my country's digital economy development exhibits a "triple helix drive" but still faces significant challenges: Regional imbalances are prominent, with digital industry growth in the eastern region reaching 6.5% while in the western region only 0.8%, leading to significant resource imbalances. Core technology sectors are highly dependent on external sources, and some key technological links present supply security risks, hindering independent development. To this end, this study proposes a three-dimensional collaborative approach: technological innovation, institutional improvement, and ecosystem optimization. This will further deepen the implementation of the "Eastern Data West Computing" project, accelerate the reform of the market-oriented allocation of data elements, break down barriers to factor mobility, and ultimately build a new landscape for high-quality development of the digital economy, providing sustained impetus for economic transformation and upgrading.

Against the dual backdrop of intensifying global food security challenges and increasingly tight resource and environmental constraints, enhancing agricultural Total Factor Productivity (TFP) has become a core driver for promoting high-quality agricultural development. Artificial Intelligence (AI), as a strategic technology leading the new round of scientific and technological revolution and industrial transformation, is profoundly reshaping agricultural production methods and industrial ecosystems. This paper systematically elucidates the driving effect of AI on agricultural TFP growth through three key mechanisms: enhancing technical efficiency, optimizing factor allocation, and fostering new business models. Simultaneously, it identifies the multiple challenges in the AI-enabled empowerment process, including underlying data deficiencies, technological application bottlenecks, institutional and talent lag, and regional disparities. To address these issues, this paper proposes systematic optimization pathways, including building a high-quality agricultural data resource system, developing adaptable AI technologies and equipment, cultivating interdisciplinary "AI + Agriculture" talent, and improving policy regulations and ethical governance frameworks. This research aims to provide a theoretical framework for understanding the intrinsic logic of AI-driven agricultural TFP growth and to offer decision-making references for formulating relevant industrial policies and promoting the practical implementation of smart agriculture.

Population aging is expected to slow economic growth. A recent paper by Maestas et al. (2023) finds that a 10% increase in the US population share of individuals aged 60+ decreases GDP per capita by 5.5%, and attributes approximately two-thirds of this decline to a slower growth in labor productivity, using the variation in the predetermined component of aging. I expand this research to all OECD nations, using a similar identification strategy. I find that a 10% increase in share of a nation’s population aged 60+ decreases labor productivity per worker by 4.95 percentage points and decreases total factor productivity growth by 0.717 percentage points. Additionally, these results are stronger for earlier members of the OECD, indicating that older nations are at greater risk of economic stagnation due to these demographic shifts.

Abstract The European Union’s (EU) Cohesion Policy aims to reduce regional disparities through the European Structural and Investment Funds (ESIFs). While previous research has documented the positive effects of ESIFs on GDP growth, the role of regional capital accumulation in the growth process remains underexplored. To address this gap, the present study investigates the impact of ESIFs on regional performance by focusing on total factor productivity (TFP) growth as the outcome variable. TFP is computed by accounting for the highly heterogeneous patterns of capital accumulation across 262 NUTS2 regions over the period 2000–2019. Using annualised regional expenditure data, we assess the influence of fund allocation independently of EU programming periods. Our empirical strategy accounts for temporal heterogeneity by distinguishing three distinct phases: pre-crisis (2000–2008), crisis (2008–2014) and recovery (2014–2019). It also considers spatial heterogeneity by classifying regions according to their level of economic development. Furthermore, we disentangle the effects of the main funds—namely, the European Regional Development Fund (ERDF), the European Social Fund (ESF), the Cohesion Fund (CF), and the European Agricultural Fund for Rural Development (EAFRD). The results indicate that ERDF is positively associated with regional TFP, particularly during the 2014–2019 period, contributing to the reduction of productivity gaps between Eastern and more advanced regions. EAFRD enhances agricultural TFP growth, although primarily in regions that already exhibit high productivity levels. The remaining funds do not show statistically significant effects. These findings underline the importance of accounting for investment heterogeneity when evaluating the effectiveness of ESIFs and contribute to the broader policy debate on regional development strategies within the EU.

Improving the green total factor productivity of the electric power industry is crucial to realize China's clean and low-carbon transition and sustainable economic growth. The super-efficient SBM model and ML index method were used to measure the green total factor productivity and its growth rate in the power industry in 30 provinces of China from 2005 to 2021, and the dynamic green total factor productivity is split to analyze the impact of technological progress and changes in technological efficiency. The spatial transfer paths and spatiotemporal differences of dynamic green total factor productivity in the electric power industry are further investigated through a spatial Markov chain model and panel Tobit model, and their influencing factors are empirically examined. The results show that: ① The static green total factor productivity of the electric power industry in each province during the study period was low and showed large differences, with considerable room for improvement. ② The green total factor productivity of China's electric power industry increased at an average annual rate of 3.9%, of which the average annual growth rate of technological progress was 3%. Among the six power grid regions, the ML index of green total factor productivity of the power industry of East China, Central China, and South China Power Grid was among the top three in China, and technological progress was the main driving force. ③ The transfer probability of the green total factor productivity level in the power industry between regions was affected by the spatial lag effect. The transfer trend of regions with different levels varied, showing a low state maintenance probability and high transfer probability. ④ Economic level, power industry capital intensity, industrial structure, and green technology innovation were the key factors for the growth of green total factor productivity in the power industry, and there was heterogeneity in the mechanisms of influencing factors in the six power grid regions.

Abstract The age distribution of Europe’s workforce has shifted towards older workers over the past few decades, a process expected to accelerate in the years ahead. This demographic trend presents significant challenges to productivity and economic growth, particularly in the EU-27, where workforce aging is projected to reduce growth in total factor productivity. This paper studies the effect of workforce aging on productivity, identifies the main transmission channels, and examines policies that could mitigate its effects. The main research question is whether the utilization of artificial intelligence is offsetting the negative impact of workforce ageing on total factor productivity. The study employs a mixed-methods approach, including quantitative analysis of Eurostat labor market data and qualitative insights derived from interviews with managers. The results of the empirical analysis suggest that AI adoption positively affects productivity, even in the context of a demographic shift associated with ageing workforce. Findings also indicate that AI can partially offset the productivity decline by automating repetitive tasks, enabling older workers to focus on higher-value activities, and facilitating continuous learning through advanced training systems. Additionally, policies aimed at reskilling older workers, promoting intergenerational knowledge transfer, and investing in AI infrastructure could further ameliorate the effects of workforce aging. To sum up, the implications of the study are that artificial intelligence utilization contributes to productivity enhancements, therefore there is a need for introducing policies that promote its adoption, as well as reskilling and upskilling of workforce to be able to follow the trends.

The 1993 publication of a World Bank book on the East Asian Miracle explained the extraordinarily rapid growth of Japan and seven other economies of East Asia (at 5 percent a year) between 1965 and 1990 as grounded in those economies’ adherence to market “fundamentals”—sound macro management, “shared” growth policies, investment in human capital—combined with an “export push” which fostered the technological learning that drove those countries’ high total factor productivity growth. The Bank authors dismissed “industrial policy” as central to their growth and cautioned against other developing countries adopting industrial policy in the absence of strong government institutions. Was the book too much a product of its post-Soviet, neoliberal era? Considering what we know now about the state of governance in developing countries, might industrial policy help boost growth in at least some developing countries?

Total Factor Productivity (TFP) represents the efficiency level of input utilization in generating output within a specific region, often reflecting the role of technological progress and innovation. This study analyzes the TFP of Surabaya City from 2011 to 2024 using the Growth Accounting Model based on the Solow Residual approach. The main variables considered are Gross Regional Domestic Product (GRDP), Labor Force (Employment), and Capital. The research adopts a descriptive-verificative method to calculate and estimate the TFP value for 2025 using trend analysis, which allows projection despite incomplete future data. The findings reveal that TFP growth in Surabaya tends to fluctuate and remain relatively low, indicating inefficiencies in combining labor and capital to generate optimal output. Interestingly, during the COVID-19 pandemic year of 2020, the city achieved a positive TFP value, suggesting that reduced input utilization improved production efficiency. These results highlight the importance of technological innovation, workforce quality, and effective policy intervention in improving regional productivity. The study emphasizes the need for the Surabaya City Government to enhance efficiency through strategic investment in technology, human capital, and policy support to sustain long-term economic growth and competitiveness.

This study attempts to examine the asymmetric effect of remittances on total factor productivity (TFP) growth in India, China, and the Philippines over 1982–2023 using a nonlinear ARDL (NARDL) benchmark specification that decomposes remittances into positive and negative shocks. The long-run nonlinear estimates show that TFP responses to remittances are state-dependent rather than uniform. Remittance adverse shocks have no significant effect in any country, suggesting that downturns are cushioned by countercyclical remitting, coping mechanisms, and diversified financing. Remittance positive shocks, however, are decisive: in India and China they boost TFP by easing liquidity constraints and supporting human and physical capital formation, while in the Philippines they reduce TFP through Dutch-disease effects, as inflows appreciate the exchange rate, shift resources to non-tradables, and weaken productivity gains. Thus, while India and China should channel remittance surges into productive investment to enhance TFP, the Philippines needs policies that curb Dutch-disease pressures and redirect inflows toward tradables, skills, and technology.