Luenberger Productivity Indicator Research Articles

THE CONTRIBUTION OF NATURAL CAPITAL ON CARBON TOTAL FACTOR PRODUCTIVITY IN CHINA

This study focuses on the contribution of natural capital to achieving carbon neutrality in China based on the provincial data from 1998 to 2019. This paper first measures the natural capital of each province by ecological footprint (EF), then explores the contribution of natural capital on carbon total factor productivity (CTFP) through a global non-radial Luenberger productivity indicator (GLPI), and decomposes the new CTFP into efficiency change (EC) and technical change (TC). We find that the contribution of natural capital to CTFP is relatively small, mainly due to the negative impact of infrastructure land and energy footprint and the decline of the marginal effect of natural capital and environmental carrying capacity. Second, technology innovation is still a main driver of promoting the CTFP of EF. Third, the contribution of natural capital to CTFP varies greatly with different types of natural capital and regions. We provide corresponding policy implications based on the main conclusions.

Urban green total factor productivity in China: A generalized Luenberger productivity indicator and its parametric decomposition

In this study, by taking the distance elasticity shares as the input weights in the production function, the generalized Luenberger productivity indicator (GLPI) was proposed to analyze the urban GTFP in China, and the stochastic frontier analysis (SFA) was used for parametric decomposition to obtain the technological change (TC), technical efficiency change (TEC), and scale efficiency change (SEC). The results show that the entire model and specific parameters passed the test, and the production function equation has good economic and statistical significance, which shows that it is feasible to use the parameter approach to measure and decompose the GLPI. Second, the value of the GLPI showed a rapid growth trend between 2000 and 2019, with a cumulative increase of 140.59%, indicating that various inputs were saved by 3.43% in a specific proportion, and that remarkable achievements have been made in the sustainable development of Chinese cities. Third, TC, TEC, and SEC all showed significant spatiotemporal heterogeneity, in which SEC was the main numerical component and TC was the driving force of the GLPI. Through mathematical proof in theory and data matching in reality, it is confirmed that the GLPI proposed in this study is scientific and applicable.

A good medicine tastes bitter: Environmental regulation that shapes China's green productivity

This study scrutinizes the effect of the Sewage Charges System (SCS) reform on the green total factor productivity (GTFP) across China's provinces. Employing the global Luenberger productivity indicator based on a non-radial directional distance function, we observe an upward trend in China's GTFP during the study period. However, the SCS reform precipitates a contraction in the GTFP, particularly pronounced in low-income and resource-based provinces of China. Empirical findings withstand a battery of robustness checks of self-selection and placebo tests. Further mechanistic tests suggest that the SCS reform influences the GTFP by escalating pollution treatment costs, curtailing R&D investment, and undermining the quality of green innovation via crowding-out effects. Overall, the SCS reform is effective in curbing SO2 emissions, but it reduces economic efficiency. Therefore, how to synergize emission reduction and economic growth is an important issue to be addressed in the area of environmental policy design in China.

Network Technology, Whole-Process Performance, and Variable-Specific Decomposition Analysis: Solutions for Energy-Economy-Environment Nexus

The sustainability of the industrial sector is often evaluated in a one-stage process. On the other hand, industrial activities are characterized by complex and multistage natures, which creates challenges for industrial performance assessment. Properly measuring industrial sustainability and understanding the driving factors (e.g., energy use or labor) of whole-process industrial operation is important for sustainable industrial sector management. To address these difficulties, we propose two new frameworks: the network variable-specific bounded-adjusted measure and network variable-specific Luenberger productivity indicators decomposition. These both take into account the whole-process context and network nature of industrial production, and unpack and quantify the contributions of specific components of the industrial process affecting sustainability. In order to capture both the status and evolution of sustainability performance, two indices are constructed. These are then decomposed to investigate the contribution of particular components to overall sustainability. The proposed approach is applied to analyze the sustainability of the industrial sector in 30 of China's provincial administrative regions between 2006 and 2015. The static sustainability inefficiency indicator results indicate that in the production and treatment process, use of the most efficient existing technology would allow a further 48.0% and 23.6% of pollutant emissions to be reduced and treated, respectively. In the production process, the most efficient technology could produce a 10.7% improvement in energy conservation. The average annual dynamic environmental performance was 2.45% and 2.07% for the production and treatment processes, respectively. There is significant heterogeneity between regions and for different variables.

A productivity indicator for adaptation to climate change

This study draws on economic index theory to construct a new indicator for adaptation to changing environmental conditions, most notably climate change, which may shift the production technology over time. Such environmental shifts are largely exogenous to firm decision making, for instance investments in research and development, which may also lead to technology change. Few existing measures of total factor productivity (TFP) make this distinction, between exogenous environmental shifts and shifts due to firm decision making or innovation. We introduce a nonparametric Luenberger productivity indicator for adaptation, which allows for decomposition of standard technology and efficiency change measures into both environmental and production components. We apply this framework to agricultural production in the US Mississippi River Basin for recent decades, working with USDA Census of Agriculture data at the county level and key climate conditions. We also match the production and climate data to estimates of Nitrogen loading over time, to incorporate water quality into the adaptation indicator. Our results indicate sustained overall productivity growth, for both agricultural production and nitrogen loading reductions, driven by technology gains outweighing efficiency losses. Decomposing further to the adaptation component, our results indicate modest overall adaptation gains, driven by both adaptation efficiency and technology gains.

Measuring and decomposing profit efficiency changes of water utilities: a case study for Chile

ABSTRACT Estimating profit inefficiency and its drivers is highly relevant for water utilities and water regulators to reduce water tariffs. We employed a novel methodological approach to compute profit inefficiency and changes to profit efficiency based on the Luenberger productivity indicator. This empirical application focused on the water industry in Chile from 2010 to 2018. Estimated average profit inefficiency was 43.6%, with the main contributor being allocative inefficiency (35.7%). In contrast, the effect of technical inefficiency was more limited (7.9%). Changes to profit efficiency differed among full private and concessionary utilities, with averages of 0.021 and 0.002, respectively.

The effect of innovation pilot on carbon total factor productivity: Quasi-experimental evidence from China

Since the policy design for carbon emission reduction must consider both the efficiency and the cost of emission reduction, improvement in carbon total factor productivity (CTFP) is widely regarded as an important economic path in reducing carbon emissions. With prefecture-level data in China from 2003 to 2016, this paper explores the role of innovation pilot on the CTFP using China's Innovative City Pilot (ICP) policy as a quasi-experiment. First, we construct a novel CTFP framework, that combines the non-parametric Luenberger productivity indicator with the biennial non-radial directional distance function, to take the slack issues and infeasibility problem into consideration. Next, with the staggered difference-in-differences model, we examine whether innovation can stimulate CTFP improvement. The results show that the ICP policy exerts a significant positive effect on CTFP, increasing cities' CTFP by 0.015 on average. Moreover, we identify five potential channels from both economic analysis and comprehensive factor decomposition: technological and low-carbon innovation, industrial upgrading, energy efficiency, carbon emission intensity, and efficiency change. These positive impacts are heterogeneous across different kinds of cities. Specifically, the effects of innovation are stronger in the eastern and central cities, large cities, and cities with tighter environmental regulations. Meanwhile, the political incentives analysis shows that in cities with higher political incentives, officials perform better in energy intensity reduction and CTFP improvement. This paper explores direct evidence for the innovation's effect on carbon productivity, which provides a novel perspective for improving carbon productivity and the future direction of innovation in China.

Comparing green productivity under convex and nonconvex technologies: Which is a robust approach consistent with energy structure?

AbstractTotal factor productivity is used to explore the input–output efficiency of the economy and the driving factors behind economic growth. Although scholars have researched the total factor productivity approach, comparisons among different models in empirical research are rare and few scholars have focused on worldwide total factor productivity gains. Using convex and nonconvex technologies, this contribution investigates green productivity gains of 129 worldwide countries during 2000–2019 based on three popular productivity measures, namely, Luenberger–Hicks–Moorsteen indicator, Luenberger productivity indicator, and Malmquist–Luenberger index, respectively. Inspired by a metafrontier approach, we compare their productivity evolutions with the energy structure among 121 economies. A negative relationship is expected between the change in the proportion of fossil fuel energy consumption and green productivity. Our results show that the Luenberger–Hicks–Moorsteen productivity indicator under nonconvex technologies is a more convincing productivity measure when considering undesirable outputs in production technology.

Revisiting rebound effects of energy use and pollutant emissions: The role of technological change

Measuring the rebound effect (RE) of energy use and pollutant emissions is fundamental to characterizing the effects of technological progress on energy conservation and emissions reduction. This paper presents a total-factor RE model that combines the Fixed effects (FE) model, Data Envelopment Analysis (DEA), and Luenberger Productivity Indicator (LPI). The proposed model takes into account two types of disposability for variable-specific decomposition for LPI. The model includes two general indices, static inefficiency performance and dynamic inefficiency performance, which can be used to assess performance in the industrial sector and measure technological change and RE. The results show that switching from natural to managerial disposability improves energy RE by 12% but reduce RE of NOx and SO2 emissions by 22% and 24%, respectively. These findings suggest that cleaner energy expansion can promote energy RE, while reducing environmental RE.

Does the high-speed railway make cities more carbon efficient? Evidence from the perspective of the spatial spillover effect

With the aggravation of carbon emissions and global warming, the probability of various natural disasters and environmental risks will increase significantly. Therefore, carbon emission reduction and low-carbon development have become the focus of environmental economic research. This paper uses the information about the opening of high-speed railway (HSR) in China to comprehensively explore the effect of infrastructure construction on urban carbon total factor productivity (CTFP) in China, which is calculated by the novel non-parametric method named biennial non-radial Luenberger productivity indicator (BLPI). Our results reveal that in China, an extra HSR line significantly improves the CTFP of local cities by 0.63%, while the CTFP of related surrounding cities increases by 6.3%. We confirm the robustness of our empirical findings and perform the mechanism analysis from several aspects. The mechanism analysis shows that the HSR improves the CTFP of local cities mainly via industrial upgrading and technological progress, and improves the CTFP of surrounding cities via industrial agglomeration and factor spillover. Besides, the impact of the HSR is more pronounced for the core cities, large cities, and innovative cities. Our results suggest that the HSR makes cities more carbon efficient, which is a non-negligible advantage of infrastructure construction in reducing environmental risks.

Temporal and spatial evolution and influencing factors of urban ecological total factor productivity in the Yellow River basin under strong sustainable development.

A growing consensus worldwide has indicated the need to protect the ecological environment and achieve sustainable development. Ensuring ecological protection and high-quality development of the Yellow River basin have become China's major national strategy. We reviewed extant literature, summarised government reports and guidance documents on the Yellow River basin, and proposed introducing a strong sustainable development theory into the study of total factor productivity (TFP). The spatial-temporal evolution and influencing factors of urban ecological TFP in the Yellow River basin are of great practical significance. We proposed a new ecological TFP indicator: the modified input-oriented Luenberger productivity indicator (MIL). Using panel data from 78 cities in the Yellow River basin during 2003-2019, we measured the urban ecological TFP. We adopted the geographic information system tool and kernel density estimation to analyse the temporal and spatial evolution of the indicator, as well as its spatial effects and influencing factors, using the global Moran's I index and dynamic spatial Durbin model (SDM). Our results show that, during the sample period, our indicator increased in cities in the region with an average annual growth rate of 0.627%, driven by technological progress. The average annual growth rate in urban areas showed a decreasing distribution of 'downstream-midstream-upstream'. Fiscal decentralisation (FD), industrial structure (IND), financial development (FIN), urbanisation level (URB) and research and development (RD) investment improved growth rates in this and the adjacent regions through direct and indirect effects. However, environmental regulation (ER), opening level (OPEN) of cities and population density (POP) were obstacles to TFP growth.

Internet technology and green productivity in agriculture.

The high-quality development of agriculture requires not only sustainable growth of agricultural productivity but also green agricultural production. Internet technology has played an essential role in agricultural production and marketing in China over the past decades. This paper estimates provincial agricultural green growth in China from 1997 to 2019 and decomposes it into technological progress (TP) and efficiency changes (EC) based on the Luenberger productivity indicator method. Then an econometric model is employed to analyze the impact of the Internet on the growth of agricultural green productivity and each sub-component, and moderating role of farmer education in such effect. The empirical results indicated that annual average growth rate of agricultural green productivity in China is 1.33% from 1997 to 2019, and technological progress dominates its growth. The development of Internet technology has a significant positive impact on agricultural green productivity and its decomposition. Farmer education has strengthened the effect of Internet technology on agricultural green productivity and its decomposition TP and EC.

Industrial energy consumption and pollutant emissions: Combined decomposition of relative performance and absolute changes

AbstractCombined decomposition of relative productivity growth and absolute changes in input/output variables helps to identify the driving forces of energy conservation and emission reduction. This paper proposes a framework based on the Luenberger productivity indicator (LPI) and the logarithmic mean Divisia index (LMDI) approach to analyze the energy–economy–environment nexus. The empirical case of the China's industrial sector for 2006–2014 is analyzed. Following this approach, we present a variable‐specific decomposition framework which attributes the overall productivity change and its sources to individual input/output variables. Empirically, we examine the relationships between the productivity growth and energy consumption within the LPI–LMDI framework across China's province‐level regions. The results show important spatial variation in the productivity measures. The LPI–LMDI decomposition implies that the industrial energy consumption increased with productivity growth appearing as the only mitigating factor. As regards the industrial SO2 emission, the individual productivity growth effect and the efficiency change effect remained as suppressing factors for emission abatement. These results can be used for evidence‐based decision making.

Generalised commensurability properties of efficiency measures: Implications for productivity indicators

We analyse the role of new weak and strong commensurability conditions on efficiency measures and especially on productivity measurement. If strong commensurability fails, then a productivity index (indicator) may exhibit a homogeneity bias yielding inconsistent and contradictory results. In particular, we show that the Luenberger productivity indicator is sensitive to proportional changes in the input-output quantities, while the Malmquist productivity index is not affected by such changes. This is due to the homogeneity degree of the directional distance function under constant returns to scale. In particular, the directional distance function only satisfies the weak commensurability axiom in general. However, if the directional distance function is a diagonally homogeneous function of the technology, then the directional distance function satisfies strong commensurability. This explains why the direction of an arithmetic mean of the observed data works well. Numerical examples and an empirical illustration are proposed. Under a translation homothetic technology, the Luenberger productivity indicator is not affected by any additive directional transformation of the observations.

Measuring China's agricultural green total factor productivity and its drivers during 1998–2019

Improving agricultural green total factor productivity (AGTFP) is essential to China's agricultural sustainable development. Although several studies have focused on China's AGTFP, its measurement and drivers are not fully investigated yet. More specifically, the published research examining the drivers of China's AGTFP at both the production and factor levels is still scarce. To fill this gap, this study constructs two different data envelopment analysis models combined with green Luenberger productivity indicator (GLPI), the biennial weight modified Russell model and the biennial bounded adjusted model, to measure China's AGTFP as well as check the robustness. We further decompose the AGTFP growth at both production and factor levels to investigate its drivers. The main findings are as follows. First, during 1998–2019, the central region with its GLPI at 0.0377 had the largest AGTFP growth, followed by the western (0.0281) and eastern regions (0.0254). Second, in terms of production-decomposition, technical progress was crucial driver to AGTFP growth, energy conservation and emission reduction (ECER) and market performance. Third, in terms of factors-decomposition, the contributions of these factors to the AGTFP growth were positive and the contribution rates ranged from 1.01% (pesticide) to 38.51% (agricultural carbon emissions). Additionally, ECER performance was the primary driver of AGTFP, accounting for about 51.35% of the growth. Finally, according to the decompositions, Porter effect was discovered in China's agricultural sector. ECER drove China's agriculture to achieve win-win development between the environment and economic production.

Decomposition of Green Agricultural Productivity Gain Under a Multiple-Frontier Framework

This paper investigates structural variation of agricultural productivity growth in China using a multiple-frontier approach. Agricultural productivity gain in Sichuan province is decomposed into technological progress, technical, mix and scale efficiency changes in terms of economic and environmental performance. The results show that agricultural Luenberger productivity indicator in Sichuan presents a significant increase trend with annual growth rate of 2.5% over the period of 1997-2017. The agricultural green growth in Sichuan is most driven by technological progress. Of the decline in the OI changes, over 90% results from the decline of environmental inefficiency. The technological progress is the main driven force of the economic growth while the TEC (technical inefficiency) incumbers its growth. The structural effects (mix and scale components) incumber environmental growth. The results imply that policymakers should pay more attention to new agricultural technological development and resource misallocation to improve agricultural green growth in Sichuan.

Integrating economic, environmental and societal performance within the productivity measurement

• The comprehensive TFP in China is calculated by integrating economic, environmental and societal performance. • A novel nonparametric approach incorporating the three dimensions is utilized. • Medical care provided the largest contribution for the increase of TFP. • China's TFP growth rates differ significantly across different regions. Total factor productivity (TFP) indicators or indices are usual measurements for evaluating economic performance in terms of output and input evolution. This approach has been extended in the environmental dimension in the literature. However, the social dimension is equally important for a comprehensive TFP, which is ignored in existing studies. Using provincial-level data from 2000 to 2017 in China, this paper applies a novel nonparametric approach incorporating three dimensions (economy, environment and society) to estimate the Luenberger productivity indicator in order to understand how to realize sustainable and high-quality development. Then, the overall productivity gain is decomposed into three different parts to evaluate economic, environmental, and social performance. The results show that the growth rate of TFP in China within the sample interval was 6.822%. Regarding its decomposition, medical care provided the largest contribution to the increase in TFP (3.840%), followed by emission reduction (1.981%), economic growth (0.975%), education (0.016%) and employment (0.010%). However, there is regional variety showing that eastern China had high-quality TFP growth (4.696%), while the TFP change was negative (-1.165%) in western China due to an inferior economy, environment, and educational development.

The impact assessment of smart city policy on urban green total-factor productivity: Evidence from China

This study systematically explored the effect of smart city policy (SCP) on urban green total-factor productivity (GTFP) in China. We first measured the urban GTFP using the Luenberger productivity indicator derived from the directional distance function, and further investigated the impact of SCP on urban GTFP using the difference-in-differences (DID) model, and its heterogeneities, impact mechanism, and spatial spillover effect were examined using balanced panel data for 283 prefecture-level cities from 2004 to 2017. The study produced five key results. (1) SCP can significantly promote urban GTFP in China, and a series of robustness checks verified the validity of this finding. (2) There was a positive spatial spillover effect of SCP on GTFP in neighboring non-pilot cities, while the spillover effect on the neighboring pilot cities was poor. (3) There was heterogeneity among Chinese cities, with the promotional effect of SCP on GTFP being more significant in eastern cities, super cities, non-resource-based cities, and cities with high levels of human capital, financial development, and information and communication infrastructure construction. (4) The regression results of a mediating effect model showed that SCP can promote GTFP through technological innovation, upgrading the industrial structure, and resource allocation optimization. (5) Smart city policy improved urban GTFP mainly by improving technical efficiency rather than technological progress. These conclusions are of great theoretical and practical significance for the Chinese government, and can be used to transform the extensive urban development mode and achieve high-quality development.

A dynamic by‐production framework for measuring productivity change in the presence of socially responsible and undesirable outputs: Evidence from European food processors

AbstractIn the past decade, corporate firms have globally increasingly adopted corporate social responsibility (CSR) practices; however, so far less is known about how productivity change in the presence of socially responsible and undesirable output can be evaluated. This paper develops a framework that integrates CSR in a dynamic by‐production technology which captures the transformation of multiple inputs into marketed outputs, socially responsible and undesirable outputs, while accounting for adjustment costs in quasi‐fixed inputs. The paper illustrates the method using a sample of European food and beverage manufacturing firms. The results of the empirical application show that there is a decline in dynamic Luenberger productivity indicators mainly due to technical inefficiency change. Hence, firms should devise strategies to enhance utilization of resources and reduce technical inefficiency. The regression of productivity change indicators on firm‐specific factors showed that more indebted firms experienced a lower growth of the productivity of undesirable output, whereas more profitable firms have a lower productivity growth of variable inputs and a higher growth of the productivity of investments. [EconLit Citations: C02, C14, C6, D24]

An evaluation of the productivity change in public transport sector using DEA-based model

This study aims to build a framework for measuring the productivity in the public transport sector through a data envelopment analysis (DEA) technique. This paper extends the Malmquist productivity index (MP1) and Luenberger productivity indicator (I.P1) evaluation with the concept of an input-oriented new slack model (NSM). NSM model measures the efficiency with the effect of slacks and satisfies unit invariance, radial and translation invariance properties. In particular, the purpose of the proposed extension is to obtain the overall productivity change in terms of technical change (Frontier Shift) and technical efficiency change (Catch-up Effect) for Rajasthan State Road Transport Corporation (RSRTC) bus depots from 2008 to 2019. For this purpose, the number of buses, number of employers, fuel consumption and route distance arc are considered input variables, while passenger-kilometres occupied and vehicle utilisation are output variables. Finally, the result demonstrates that the average total factor productivity (TFP) growth of 46 depots using MPI and LPI over the study period is 1.956% and 1.409%, respectively. This study enables policy-maker and managers to evaluate the input to reach consistent output up to an optimum level and understand the process of improving the productivity level for the bus depots.