Energy taxes, energy innovation, and green sustainability: empirical analysis from a China perspective.

The idea that energy taxes and innovation may contribute to lowering greenhouse gas emissions and fostering the development of a more sustainable energy future is gaining popularity. Therefore, the study's main goal is to explore the asymmetric impact of energy taxes and innovation on CO2 emissions in China by employing linear and nonlinear ARDL econometric methods. The outcomes of the linear model demonstrate that long-term increases in energy taxes, energy technological innovation, and financial development cause CO2 emissions to reduce, while increases in economic development cause CO2 emissions to climb. Similarly, energy taxes and energy technological innovation cause CO2 emissions to fall in the short run, while financial development promotes CO2 emissions. On the other hand, in the nonlinear model, the positive energy changes, positive energy innovation changes, financial development, and human capital help reduce the long-run CO2 emissions, and economic development increase the CO2 emissions. In the short run, the positive energy and innovation changes are negatively and significantly connected to CO2 emissions, while financial development is positively linked to CO2 emissions. The negative energy innovation changes are insignificant in both the short and long run. Therefore, Chinese policymakers should try to promote energy taxes and innovations as tools to achieve green sustainability.

Model development and update of portable NIRS instrument for assessment of internal quality attributes of two navel orange varieties.

The variation of fruit among batches influences the performance of the portable near infrared spectroscopy (NIRS) instrument and then determines the success or failure for practical application in fruit industry. Model development and update methods were investigated for determining soluble solids contents (SSC) and titrable acidity (TA) of navel orange. The pretreatment and variable selection methods were explored for building partial least square regression (PLSR) models. The best models, developed by the combination of second derivative (2D) and variable sorting for normalization (VSN), could predict SSC but not TA. The root mean square error of prediction (RMSEP), coefficient of determination for prediction ( R p 2 ) and ratio of prediction to deviation (RPD) for SSC were 0.66 °Brix, 0.66 and 1.73. Model maintain methods of model update (MU) and slope and bias correction (SBC) achieved the best results in predicting SSC for two external validation sets with R p 2 , RMSEP and RPD of 0.54, 0.83 °Brix, 1.60 and 0.52, 0.83 °Brix, 1.65, respectively. The results suggested model development and update with MU and SBC could improve the robustness of the portable NIRS instrument in predicting SSC of navel orange.

Global temporal evolution of CH4 emissions via geo-economic integration.

Globalization has characterized geo-economic integrations of world regions via South-South, North-North, and South-North trades, which play a critical role in displacing global greenhouse gas emissions. Based on the global CH4 emission inventories from the EDGAR database and the global multi-region input-output accounts from the EORA database, this study explores the trade-induced CH4 emission transfers of 20 geographical regions of the world from 1990 to 2015. Global total CH4 emissions increased by 19.13% in 2015 compared to 1990, while trade-related emissions increased by 46.28% over the same period. Western Europe, the USA, Japan, Other East Asia, and Mainland China were the largest five importers of embodied CH4 emissions, while Sub-Saharan Africa, Russia, Middle East, Mainland China, and Southeast Asia were the largest five export regions of embodied CH4 emissions. Substantial agriculture- and energy-related CH4 emissions were transferred from developed regions to developing regions. The trade between economies from the global south and the global north had undergone positive changes, with the trade structure and transfer path showing a trend of divergence. Among the total CH4 emissions embodied in international trade, the CH4 emissions embodied in South-North trade accounted for more than half (55.94-62.72 Tg, 71%-55%) from 1990 to 2015. The CH4 emissions embodied in the South-South trade accounted for 19%-34% (14.62-39.46 Tg), and the proportion of CH4 emissions embodied in the North-North trade appeared to be relatively small (10%-11%, 8.05-12.82 Tg) during the period. It is imperative to strengthen South-South, South-North, and North-North cooperation in multilateral trade to jointly cut down the CH4 emissions among world regions.

Uneven development within China: Implications for interprovincial energy, water and arable land requirements.

Owing to uneven development and unbalanced resource endowments within China, ensuring reliable energy, water and food supply is a core challenge to regional socio-economic development. This study makes a first attempt to examine and compare demand-driven energy, water and arable land (E-W-L) resource outsourcings within China based on the latest multi-regional input-output model. Results show that interprovincial trade reallocated 73.4%, 33.9% and 38.1% of the national total E-W-L resource inputs in 2012, respectively. Investment was the dominant final demand category for driving energy requirements, while consumption was the leading final demand category for water and arable land requirements. Important provincial regions and critical transmission sectors for the trade of embodied E-W-L resources are identified. Substantial E-W-L resources were transferred from the central and western regions to the eastern regions. Especially, Inner Mongolia was the top interregional net exporter of embodied energy, while Jiangsu topped the net importer list. Regarding virtual water transfer, Xinjiang and Shandong were the biggest interregional net exporters and net importers, respectively, while Heilongjiang and Guangdong stood out in the net trade of embodied arable land. Owing to the impact of interprovincial trade, the resource occupancy levels of the eastern developed area were much higher than those of the northeastern, central and western areas. The imbalances in the levels of socio-economic development amongst provincial regions are mirrored by their patterns of E-W-L uses and related trade transfer. Understanding the synchronal outsourcings of E-W-L resource requirements provides important implications for targeted resource management in Chinese interprovincial supply chains.

Outsourcing natural resource requirements within China.

Consumption demands are final drivers for the extraction and allocation of natural resources. This paper investigates demand-driven natural resource requirements and spatial outsourcing within China in 2012 by using the latest multi-regional input-output model. Exergy is adopted as a common metric for natural resources input. The total domestic resource exergy requirements amounted to 125.5 EJ, of which the eastern area contributed the largest share of 44.5%, followed by the western area (23.9%), the central area (23.0%) and the northeastern area (8.6%). Investment was the leading final demand category, accounting for 52.9% (66.4 EJ) of national total embodied resource use (ERU). The total trade volumes of embodied resource were equivalent to 69.6% of the total direct resource input (DRI), mostly transferred from the central and western regions such as Inner Mongolia, Shanxi, Shaanxi and Xinjiang to the eastern regions such as Jiangsu, Zhejiang, Guangdong and Shanghai. The northeastern and eastern areas had physical net imports of 1213.5 PJ and 38452.6 PJ, while the central and western inland areas had physical net exports of 6364.5 PJ and 33301.5 PJ, respectively. Shanghai, Beijing, Zhejiang, Jiangsu and Guangdong had prominent ERUs which respectively were 101.6, 12.6, 11.7, 8.4 and 4.3 times of their DRIs. The ERUs of Inner Mongolia, Shaanxi, Shanxi, Ningxia and Guizhou were equal to only 17.6%, 25.3%, 27.9%, 46.0% and 50.2% of their DRIs, respectively. Regional uneven development resulted in imbalanced resource requirements across China. The findings can provide a deep understanding of China's resource-driven economic development mode, and contribute to reducing regional resource footprints and their environment outcomes under the "new normal economy".

[Detection of firmness and surface color of pear by near infrared spectroscopy based on Monte Carlo uninformative variables elimination method].

In the present study, NIRS was applied to nondestructive and rapid measurement of firmness and surface color of pear. In order to improve the prediction precision and eliminate the influence of uninformative variables on model robustness, Monte Carlo uninformative variables elimination (MC-UVE) and Monte Carlo uninformative variables elimination based on wavelet transform (WT-MC-UVE) methods were proposed for variable selection in firmness and surface color NIR spectral modeling. Results show that WT-MC-UVE can reduce the modeling variables from 1451 to 210, and get similar prediction results for firmness. WT-MC-UVE improved the prediction precision for surface color, the root mean square error of prediction (RMSEP) and calibration variables were reduced from 1.06 and 1451 to 0.90 and 220 respectively, and the correlation coefficient (r) was improved from 0.975 to 0.981. The proposed method is able to select important wavelength from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis of firmness and surface color.