Abstract
The research on international trade competitiveness is progressing continuously. Environmental factors have been gradually considered in the competitiveness of international trade. However, the green assessment system of international trade competitiveness is not perfect. Building a model based on the trade economy is complex. This study combines environmental pollution data based on the forest processing industry with trade flows. Environmental trade competitiveness, pollution treatment, and trade scale were selected as the three criterion levels to construct an assessment system. The weight and score of each index were calculated by the overall entropy method. The overall entropy method is more comprehensive than the traditional entropy weight method due to introduce longitudinal comparisons of time and category. This method is a dynamic evaluation model with analysis of three-dimensional sequential data tables. The use of this method enables the assessment model to analyze more comprehensively the green level of a country’s trade in wooden forest products in terms of time and product category. The green level of chemical wood pulp and sawn timber trade in China is at a high level. The pollution treatment and trade scale of chemical wood pulp and sawn timber attained a medium level of matching. The trades in particle board, hardboard, newsprint, carton board, and wrapping paper are at medium levels of green. The trades in medium density fiberboard and plywood have poor levels of green and need to improve their green production capacity. It is suggested that China should increase investment in scientific research, as well as establish policies to restrict and treat pollution in the industry of wooden forest products, while increasing the export volumes of products with high added value. China should attach importance to the pollution resulting from the manufacture of wooden forest products. The state should support policies for these producers reducing production emissions.
Funder
Philosophy and Social Science Research Programme of Hei Long-jiang Province, China