The relationship between technological innovation and green transformation efficiency in China: An empirical analysis using spatial panel data

Challenged by climate change and severe damage of the ecological environment caused by carbon emissions, the process of green transformation in the world has to be urgently accelerated. This study adopted the super-efficiency DEA method to calculate the green transformation efficiency (GTE) of 30 provinces of China (except for Tibet) from 2009 to 2018. From the perspective of the technological innovation process, a spatial Durbin model is further proposed to investigate the impacts of technology research and development (TERD) and technology commercialization (TECO) on GTEs. The results showed that: (1) both TERD and TECO exerted significant positive effects on GTEs in local provinces, while the TERD coefficient was higher than that of TECO. (2) The spillover effect of TERD on GTEs in neighboring provinces was significantly negative, while the spillover effect of TERD was significantly positive. (3) Although the interaction between TERD and TECO positively affected GTEs, it also exerted a negative spillover effect on GTE. These findings indicate that the government should include the spatial spillover effects of TERD and TECO on GTEs when designing policies in support of technological innovation activities that contribute to green transformation.     

Refined-scale panel data crash rate analysis using random-effects tobit model

Random effects tobit models are developed in predicting hourly crash rates with refined-scale panel data structure in both temporal and spatial domains. The proposed models address left-censoring effects of crash rates data while accounting for unobserved heterogeneity across groups and serial correlations within group in the meantime. The utilization of panel data in both refined temporal and spatial scales (hourly record and 1-mile roadway segments on average) exhibits strong potential on capturing the nature of time-varying and spatially varying contributing variables that is usually ignored in traditional aggregated traffic accident modeling. 1-year accident data and detailed traffic, environment, road geometry and surface condition data from a segment of I-25 in Colorado are adopted to demonstrate the proposed methodology. To better understand significantly different characteristics of crashes, two separate models, one for daytime and another for nighttime, have been developed. The results show major difference in contributing factors towards crash rate between daytime and nighttime models, implying considerable needs to investigate daytime and nighttime crashes separately using refined-scale data. After the models are developed, a comprehensive review of various contributing factors is made, followed by discussions on some interesting findings.     

A quantitative analysis of determinants of non-citation using a panel data model

The term “non-citation factor” refers to the percentage of never-cited papers in a citation time window, a common phenomenon in the science world. Some scholars have qualitatively explored the reasons for not citing a publication, and quantitatively analyzed the mathematical functional relations between the “non-citation factor” and “impact factor of a journal.” This study simultaneously considers the mutual relations and closeness degree between the “non-citation factor” and different influencing factors from a novel perspective—that of using a more structuralized panel data model. The analysis revealed that the determinants, including “impact factor of journal,” “age of journal,” “average number of references per paper in journal,” and “issues of journal,” exerted an extremely small but positive influence (<?0.025) on the decline of “percentage of never-cited papers in the citation time window of publication year or 3 years.” That means the improvement of these determinants can decrease the percentage of never-cited papers. The “impact factor of the journal” always had the biggest positive influence, while the “average number of references per paper in journal” always had the smallest positive influence. In wider citation time windows of 3 or 6 years, two determinants—“number of publications in journal” and “amount of interdisciplinarity in journal”—began to exert a negative effect with a positive correlation coefficient on the decline of the “non-citation factor.” That means the improvement of these two determinants cannot decrease the value of the “non-citation factor,” even though they can increase its value. It is worth noting that the “impact factor of the journal” had a positive influence on the decline of the percentages of never-cited papers in the citation time window of publication year or 3 years, and began to play a negative role in the decline of percentage of never-cited papers in the citation time window of 6 years. Finally, three variables—“average number of authors per paper in journal,” “average number of references per paper in journal,” and “issues of journal”—no longer exerted an influence on the decline of percentages of never-cited papers in the citation time window of 6 years, while “age of journal” and “average number of pages per paper in journal” still made a positive contribution. Our findings could help research institutions, researchers, editors, and publishers understand the positively or negatively influential factors that lead to non-citation, thus improving the chance of papers being cited and having some academic influence.     

Data envelopment analysis of AGV fleet sizing at a port container terminal

A decision-making approach based on Data Envelopment Analysis (DEA) for determining the efficient container handling processes (considering the number of employed Automated Guided Vehicles (AGVs)) at a port container terminal (PCT) is presented in this paper. Containers are unloaded from the ship by quay cranes and transported to the storage area by AGVs. We defined performance measures of proposed container handling processes and analysed the effects when changing the number of AGVs. The values of performance measures were collected and/or calculated from simulation. Container handling process, with a fixed number of quay cranes, when a different number of AGVs is used to transport containers from berth to assigned locations within storage area, represents a decision-making unit (DMU). We applied the basic CCR (Charnes, Cooper and Rhodes) DEA model with two inputs: average ship operating delay costs and average operating costs of employed equipment at a PCT, and two outputs: average number of handled import containers per ship and weighted average utilisation rate of equipment at a PCT. DEA method proved to be useful when testing different DMUs and when determining efficient DMUs for planning purposes. This study shows that efficiency evaluation of AGV fleet sizing and operations is useful for planning purposes at PCTs.     

DEA based on strongly efficient and inefficient frontiers and its application on port efficiency measurement

Data envelopment analysis (DEA) is a non-parametric analytical methodology widely used in efficiency measurement of decision making units (DMUs). Conventionally, after identifying the efficient frontier, each DMU is compared to this frontier and classified as efficient or inefficient. This paper first introduces the strongly efficient frontier (SEF) and strongly inefficient frontier (SIF), and then proposes several models to calculate various distances between DMUs and both frontiers. Specifically, the distances considered in this paper include: (1) both the distance to SEF and the distance to SIF, where the former reveals a unit’s potential opportunity to become a best performer while the latter reveals its potential risk to become a worst performer, and (2) both the closest distance and the farthest distance to frontiers, which may provide different valuable benchmarking information for units. Subsequently, based on these distances, eight efficiency indices are suggested to rank DMUs. Due to different distances adopted in these indices, the efficiency of units can be evaluated from diverse perspectives with different indices employed. In addition, all units can be fully ranked by these indices. The efficiency of 24 major Asian container ports is analyzed with our study, where the potential opportunities and potential crises of these ports are revealed and some new insights about their efficiency are provided.     

Transmuting performance on manufacturing dimensions into business performance: An exploratory analysis of efficiency using DEA

This research examines efficiency in the transformation of performance on manufacturing competitive dimensions into business performance in the automotive supplier industry. Specifically, the study explores efficiency produced from data envelopment analysis (DEA) using cost, quality, time, flexibility and innovativeness performance as inputs and return on investment (ROI) and return on assets (ROA) as outputs. First tier suppliers to the 'Big 3' in North America were sampled. The 51 suppliers were categorized into three groups (high, medium and low) based on their efficiency. No significant group differences in quality, time, flexibility and innovativeness performance were found, but a significant difference does exist for cost. The results were further investigated by testing between-group differences for various cost reduction programmes, thereby identifying the levers and benchmarks for improving low efficiency firms.     

Assessing sustainability of Islamic countries via data envelopment analysis (DEA)

Clean Technologies and Environmental Policy - Given international regulations, countries try to improve their sustainability. The objective of this paper is to assess the sustainability of Islamic...     

Investigation of seasonal variations of Beijing pavement condition data using unevenly spaced dynamic panel data model

Beijing municipal highway administration started to collect pavement condition data on its major expressways since 2006. It is advised in the Chinese practice standard that data collection shall be conducted on annual basis. However, pavement data are usually collected at different seasons of the year, which may cause significant seasonal variations in the observed condition. Moreover, for some reasons, data are missing for some of the pavement sections at certain years, which could bring up difficulties in performance model estimation and inference. These concerns have been simply neglected in past practice. This study proposed an unevenly spaced dynamic panel data model to investigate the seasonal patterns of a performance indicator called Ride Quality Index (RQI). A quasi-differencing approach was adopted for the estimation. Data collected from the 5th Ring Road of Beijing were used in the case study. It was found that RQI data collected during the fall season are expected to be lower than that collected during the spring or summer seasons. Findings from this research would be helpful to pavement engineers in using unevenly spaced pavement condition data for future condition estimation.     

TFT-LCD industry performance analysis and evaluation using GRA and DEA models

In this study we propose a four-stage approach, which includes data envelopment analysis, Malmquist productivity index (MPI), entropy method and grey relation analysis (GRA), to investigate the operational performance of six thin film transistor liquid crystal display (TFT-LCD) companies in Taiwan during 2009–2012. The input variables are fixed assets, operating expenses, R&D expenses and number of employees, while the output variables are cash flow and net sales. The empirical results showed that companies AUO and HannStar could increase their operation efficiency by improving their VRS efficiency and scale efficiency. When using the MPI model to measure the productivity changes for these TFT-LCD companies, we found that the technology changes in most of the companies are downward tendencies during 2009–2012 except for Ampire. Thus, not only could the proposed GRA with entropy weights evaluate the current performances of each firm effectively, it can also predict their future performances.     

The motivations for knowledge transfer across borders: the diffusion of data envelopment analysis (DEA) methodology

To facilitate technology development, people rely on quick and intensive knowledge interactions without barriers. However, when people need to transfer knowledge from one place to another, geographical distance is a critical barrier to overcome because tacit and invisible characteristics are embedded in certain knowledge and locations. This study explores how social and scientific resources embedded within persons can motivate personal knowledge-diffusion behaviors; that is, bridging resources between locations. To explain cross-border diffusion, this work analyzes knowledge dissemination of the data envelopment analysis (DEA) method. By collecting theoretical and application papers in DEA methodology from the Web of Science data set, this study analyzes the academic network consisting of 610 researchers and identifies author locations, research disciplines, and their mutual linkages to explain the importance of personal specific characteristics in cross-border diffusion. Regression models and network analysis show the advantages of personal research seniority and cross-disciplinary coordinating capabilities for researchers to diffuse knowledge from one region to another. The corresponding brokering capabilities accumulated within domestic area or adjacent nations are also helpful for specifically brokering resources of other farther places.     

Using the frontier production function and minimax approaches in measuring productive efficiency: Critical remarks

This paper shows that Aigner and Chu's frontier production function (FPF) method is, at best, a special case of the Farrell method. That is, the FPF method implicitly creates a hypothetical decision-making-unit (DMU) from the original data and uses the frontier evaluating the hypothetical DMU to evaluate all the DMUs, which may make the estimated frontier, efficiency scores and the ranking of DMUs unidentifiable. The paper also shows that one of Sengupta's minimax methods is also at best a special case of the Farrell method (i.e., it uses the frontier evaluating the most inefficient DMU to evaluate all the DMUs), and Sengupta's other minimax method cannot produce meaningful efficiency scores and production frontiers. The Farrell method, which uses the whole piecewise linear frontier to evaluate DMUs, is concluded to be better than both the FPF and Sengupta's methods. Received: 27 May 1997 / Accepted: 16 December 1997     

Motivating business towards innovation: A panel data study using dynamic capability framework

National strategic dynamic capabilities refer to the nations' ability to acquire, develop, and transfer national resources into innovation. This would help provoke firms and individuals’ capabilities to sense and seize to expedite innovative performance transformation. This study suggests that through the dynamic capabilities (DC) framework, the government can increase Innovation by channelizing the economic innovation system indicators. Macroeconomic indicators from 62 countries were taken to assess the significance of indicator-based dynamic capabilities model. The results confirm that sensing and seizing indices motivate Innovation, while the cost of business negatively moderates sensing. This model suggests that governments can motivate Innovation by exploring the indicators which are determining the sensing and seizing environment of decision-makers in businesses.     

Inter-country R&;D efficiency analysis: An application of data envelopment analysis

This study examines the relative efficiency of the R&D process across a group of 22 developed and developing countries using Data Envelopment Analysis (DEA). The R&D technical efficiency is examined using a model with patents granted to residents as an output and gross domestic expenditure on R&D and the number of researchers as inputs. Under CRS (Constant Returns to Scale), Japan, the Republic of Korea and China are found to be efficient, whereas under the VRS (Variable Returns to Scale) framework, Japan, the Republic of Korea, China, India, Slovenia and Hungary are found to be efficient. The emergence of some of the developing nations on the efficiency frontier indicates that these nations can also serve as benchmarks for their efficient use of R&D resources. The inefficiency in the R&D resource usage highlighted by this study indicates the underlying potential that can be tapped for the development and growth of nations.     

Scheduling operations of a rail crane and container deliveries between rail and port terminals

This article addresses the problem of scheduling container transfer operations in rail terminals. The overall problem can be divided into three smaller problems: constructing a dual-cycle delivery task by matching inbound and outbound containers; determining parking positions for trucks; and sequencing the delivery tasks for transfer by the rail crane. This article provides a mathematical model to obtain the optimal solutions to these problems, and suggests genetic and heuristic algorithms to reduce computational time when solving larger problems. Numerical experiments are conducted to validate the performance of the suggested heuristic algorithms.     

基于数据包络分析方法的数据中心能效评价

数据中心面临能源消耗量急剧上升的问题,需要研究绿色节能的能效评价方法。首先探讨数据包络分析方法在数据中心能效评价中可行性、应用方式与建模方法,进一步提出基于数据包络分析的数据中心能效评价方法,并通过对该分析方法的应用,获得不同数据中心能效结果。研究结果表明:数据包络分析方法可克服目前数据中心机房通常采用单一指标参数的局限性,不用对输入元素权重进行主观分析,可推广到其他多因素的性能测试与评价方法中。     

Performance modelling of a solar road panel prototype using finite element analysis

Performance prediction is a critical step towards the acceptance of a new pavement structure. This is true for both conventional and innovative designs; however, it is particularly important for innovative designs that attempt to redefine pavement design practices. One such innovative design concept is the solar road panel; a road panel with a transparent surface that generates electricity through embedded solar cells. Despite the work completed by multiple organisations towards the development of this concept, questions exist about the viability of these panels as a structural pavement surface. This paper investigates these questions through a finite element modelling approach that assesses a prototype panel's performance on a variety of structural bases. Overall, this paper finds that it is possible to design a solar road panel to withstand traffic loading and that a concrete structural base allows for substantial optimisation to the analysed prototype design.     

Covariance analysis and fitting of germanium gamma-ray detector efficiency calibration data

The measurement of neutron-induced reaction cross sections often requires detection of either prompt gamma rays or gamma rays emitted during the decay of radio nuclides. This paper describes the analytical procedures we use to generate calibrations for germanium gamma-ray detector full-energy-peak efficiency ε versus photon energy E, and to predict the associated uncertainties. Our method, which involves fitting a parameterized regression formula to measured data by the principle of least squares, is widely applicable beyond the specific problem considered here. It differs from most commonly used methods in that comprehensive use is made of available information on all known sources of random and systematic (correlated) error associated with the calibration process. This is accomplished through the formation and application of a calibration-data covariance matrix. Objective prediction of the errors in subsequently derived quantities (e.g., detector efficiencies at energies not directly represented in the calibration data) is then achieved through error propagation. Specifically, this paper discusses our experiences in fitting the particular empirical formula In ε = Σ_k−1^m pk(InE)^k−1 to measured calibration data at gamma-ray energies from just above 200 keV to several MeV. A numerical example is provided to demonstrate the utility of this approach.