企业金融化与绿色转型研究：机制识别与政策优化

实体企业金融化往往会对其经营管理决策产生重大影响。本文基于实体上市企业年报和财务数据（2007—2020年）,构建其绿色转型指标,进而从理论和实证两个方面确证了金融化对绿色转型的影响与机制问题。实证结果表明,企业金融化会对其绿色转型产生显著的负面冲击,其中短期金融化行为的影响更为明显;特别地,这一冲击对重污染企业、战略性新兴企业绿色转型的影响更为显著。通过机制识别,研究发现上述冲击影响主要通过恶化财务状况、提升企业风险并降低企业预期等渠道导致企业绿色转型水平下降。进一步地,通过政策优化（如金融综合改革试验区制度）支持,上述负面效应将被显著削弱,这也说明金融体制机制的优化是降低企业金融化负面效应,提升其转型升级动力和意愿的重要路径。     

90Sr、137Cs在某种包气带土壤中的迁移研究

本文叙述了放射性废物中具有代表性的裂变核素^90Sr、^137Cs在某种包气带土壤中的迁移情况研究。使用小型土柱的氚水淋洗实验研究土壤水力学性质,最后进行大型土柱实验研究核素在该包气带土壤中的迁移,并对实验情况进行了数学模拟。用该土壤原状土进行小型土柱的氚水淋洗试验,测得了土壤的水力弥散度为0．32cm,土壤有效孔隙度为0．35。经过290天的大型柱迁移试验表明,土壤对^90Sr的阻滞系数为220．4,在模拟实际降雨量的情况下,^90Sr的平均迁移速度为0．63cm/y,^137Cs在大型柱试验中没有明显迁移。数值模拟^90Sr、^137Cs迁移,得出经过上述大型柱试验相同的条件下,^90Sr、^137Cs迁移的峰位置基本和大型柱试验结果相同。     

开发区土地资源利用中存在的问题及其对策

本文通过发现问题、分析原因的方式,初步总结了我国开发区土地资源利用存在的主要问题。同时根据开发区环境评价现状以及我国现行的开发区政策和环境影响评价政策,提出了一些解决问题的建议和对策,以期为解决目前突出的开发区土地资源利用问题提供一些参考。     

成都高新区创建国家生态工业示范园区的思考

运用循环经济、工业生态学等原理,针对成都高新区创建国家生态工业示范园区存在的问题,提出由规划入手,包括企业内部、企业之间和园区三个层面的创建措施,并为近期重点建设工程指明了方向。企业内部,抓节能减排,推广清洁生产;企业之间,抓住绿色招商的契机,启动"补链"战略,提高产业关联度,构建工业生态链(网),提高园区的生态效率;园区层面,加强人才建设,健全信息网络系统。     

江西九瑞地区铜多金属矿整装勘查区成矿规律与找矿靶区优选

江西九瑞地区铜多金属矿整装勘查区为全国47个整装勘查区之一,是我国重要的铜金铅锌矿集区。通过对成矿地质条件分析和最新地质找矿成果研究,本文总结了区内成矿规律、北西向构造、燕山期花岗闪长斑岩等为区内铜金多金属矿成矿重要条件,同时认为北东东向构造-岩浆岩带为重要的成矿远景区,并优选出城门山矿区及外围、宋家湾-武山矿区及外围、彭山地区、邓家山-通江岭、宝山-大桥地区、大浪-洋鸡山丁家山等铜金铅锌矿找矿靶区。     

ENHANCEMENT OF RIPARIAN ECOSYSTEMS WITH CHANNEL STRUCTURES1

ABSTRACT: Naturally occurring and man-made structures can be used for enhancing the development of riparian zones. Naturally occurring structures are cienagas, beaver dams, and log steps. Man-made structures include large and small channel structures and bank protection devices. All these structures affect streamflow hydraulics and sedimentation and can create a more favorable environment for riparian zone establishment. However, when they are used improperly, they can be destructive to existing riparian zones. Since stream processes are generally slow, long-time spans may pass before the effects of management action, good or bad, become visible. Also, the effects of large dam installations may appear a long distance downstream from the dam. Therefore, investigations must be of a wide scope. Interactions between riparian site, channel, and streamflow may be so complex that an interdisciplinary approach is required.     

Riparian Ecosystem Management Model: Sensitivity to Soil,Vegetation, and Weather Input Parameters1

Abstract: The Riparian Ecosystem Management Model (REMM) was developed by the U.S. Department of Agriculture‐Agriculture Research Service (USDA‐ARS) and its cooperators to design and evaluate the efficiency of riparian buffer ecosystems for nonpoint source pollution reduction. REMM requires numerous inputs to simulate water movement, sediment transport, and nutrient cycling in the buffer system. In order to identify critical model inputs and their uncertainties, a univariate sensitivity analysis was conducted for nine REMM output variables. The magnitude of each input parameter was changed from ?50% to +50% from the baseline data in 12 intervals or, in some cases, the complete range of an input was tested. Baseline model inputs for the sensitivity analysis were taken from Gibbs Farm, Georgia, where REMM was tested using a 5‐year field dataset. Results of the sensitivity analysis indicate that REMM responses were most sensitive to weather inputs, with minimum daily temperature having the greatest impact on the nitrogen‐related outputs. For example, the 100% change (?50% to +50%) in minimum daily temperature input values yielded a 164.4% change in total nitrogen (N), a 109.3% change in total nitrate (NO₃), and a 127.1% change in denitrification. REMM was most sensitive to precipitation with regard to total flow leaving the riparian vegetative buffer zone (199.8%) and sediment yield (138.2%). Deep seepage (12.2%), volumetric water content (24.8%), and pore size index (6.5%) in the buffer soil profile were the most influential inputs for the output water movement. Sediment yield was most sensitive to Manning’s coefficient (46.6%), bare soil percent (40.7%), and soil permeability (6.1%). For vegetation, specific leaf area, growing degree day coefficients, and maximum root depth influenced the nitrogen related outputs. Overall results suggest that because of the high sensitivity to weather parameters, on‐site weather data is needed for model calibration and validation. The model’s relatively low sensitivity to vegetation parameters also appears to support the use of regional vegetation datasets that would simplify model implementation without compromising results.