Anhui University of Finance and Economics

About: Anhui University of Finance and Economics is a education organization based out in Bengbu, China. It is known for research contribution in the topics: China & Hopf bifurcation. The organization has 933 authors who have published 1070 publications receiving 11500 citations. The organization is also known as: AUFE.

Transferability of Vis-NIR models for Soil Organic Carbon Estimation between Two Study Areas by using Spiking

Abstract: Visible and near infrared (Vis-NIR) spectroscopy technique has been shown to be a cost-effective alternative for rapidly analyzing soil organic carbon (SOC). However, great challenges remain when applying a Vis-NIR model for SOC estimation developed in one study area to other study areas without further calibration. The scope of this study was to use spiking strategy to improve the transferability of Vis-NIR models between two study areas. Specifically, we explored the optimal spiking subset by adding different quantities of spiking samples to construct different-sized models, and the strategy of spiking with extra-weighting was used for comparison. Soil data was acquired in two independent study areas (WH area and HH area) in Hubei Province, Central China. The reflectance spectra and SOC contents were measured in the laboratory. Partial least squares regression (PLSR) was used for model calibration. The representativeness of the spiking samples was assessed through the absolute difference between the selected sample variance (s²) and the original variance (σ²) in the principal component space derived from soil spectra. Results showed that the initial models yielded successful SOC predictions for the soil samples from the same area as the calibration samples, but failed in those samples from the other area. Spiking improved the model transferability between these two study areas. Approximately 33%/48% of the HH/WH calibration set was required as spiking samples in model calibrations and applications in the other area. Spiking with extra-weighting was of limited use in small-sized spectral libraries. The use of |s²–σ²| is potentially effective in identifying the optimal spiking samples to improve model transferability between different small-sized study areas in the Vis-NIR assessments of SOC.

Energy-carbon performance and its changing trend: An example from China’s construction industry

Abstract: Understanding energy-carbon performance (ECP) and its intrinsic characteristics is important for energy saving and emissions reduction in the construction industry. However, few studies have focused on energy or carbon performance simultaneously and their pattern over time. To address this gap, this study investigated the ECP in China’s construction industry and its convergence characteristics across various provinces. We first constructed an ECP index (ECPI) using non-radical directional distance functions for the construction industry during 2003–2016. We then analyzed the convergence of ECPI using the convergence method proposed by Phillis and Sul (2007). The results showed that the overall ECPI was generally stable with some fluctuations over the sampling period, and there was no evidence to support the occurrence of convergence in the ECPI across the sample set. Furthermore, two club convergences of ECPI were recorded. Finally, we propose valuable suggestions for policymakers based on empirical results.

Economic and Environmental Impacts of Mass Tourism on Regional Tourism Destinations in Indonesia

Abstract: The study examines economic and environmental impacts of mass tourism on regional tourism destinations, particularly the establishment of “Ten New Bali”, in Indonesia. The sample is restricted to the period of time in which annual data is available and comparable among variables from 1980 to 2015 (36 observations). All of the time series data was collected and retrieved from the World Development Indicator database published by the World Bank. This study applies cointegrating regression analysis using the fully modified OLS, canonical cointegrating regression, and dynamic OLS. The results of the study suggest that 1) there is a long-run equilibrium relationship between tourism receipts, environmental degradation and economic growth in Indonesia, 2) tourism growth and agriculture land growth are positively related to an increase of total output in the short-run in Indonesia, and 3) arable land is significant at the 0.01 level, but forest rents and CO2 from transport are not significant in the short-run in Indonesia. The results confirm that arable land is negatively related to an increase of total output in Indonesia. That is, when tourism growth in the economy is getting realized it shows that the environmental degradation increases greatly in inverse in the model, eventually negative impacts to the environment.

Analysis of industrial eco-efficiency and its influencing factors in China

Abstract: Industry is the largest sector for energy consumption and pollution emissions in China. Thus, improving industrial eco-efficiency is necessary for China to achieve sustainable development. Based on panel data from 31 industrial sectors from 2001 to 2015, a three-stage data envelopment analysis model was used to empirically explore industrial eco-efficiency and its influencing factors from the perspective of industrial heterogeneity. The results show that the overall level of industrial eco-efficiency in China is not high, first declining and then rising during the study period. Low eco-efficiency was mainly due to low scale efficiency. After removing the influences of external environmental factors and noise, industry profit rates, ownership structures, and foreign direct investments were all significantly and positively correlated with eco-efficiency. Environmental regulations were significantly and negatively correlated, while the intensity of research and development exhibited no linear relationship. Industrial heterogeneity significantly affects eco-efficiency. Capital-intensive industries had the highest eco-efficiencies, followed by resource-intensive industries and labor-intensive industries, respectively. Comparison of technical efficiency (TE) before and after adjustment on a panel of 31 industries in China from 2001 to 2015.