我国环境保护必须打攻坚战和持久战

我国自然环境优越但很脆弱,人均自然资源较少,生态破坏很严重,生态欠帐很多。目前我国在科技水平还比较低的条件下进行大规模的工业化、城市化也对环境产生巨大冲击,产生一些新的环境问题。因此我国环境保护具有长期性和艰巨性,必须坚持不懈地努力,打攻坚战和持久战。     

Building partnerships — the Ohio Materials Exchange

This paper discusses a method by which individuals working in government, private business, not-for-profit and educational organizations developed a collaborative statewide program involving the exchange of materials from manufacturers, commercial business and not-for-profit organizations in Ohio.     

大型碳化硅生产园区面源源强及减排指标核算

以某年产25万吨大型碳化硅园区无组织排放面源为例,提出基于多个地面站气象数据的CALPUFF模型地面浓度反推方法,优化流场模拟,使得无组织面源源强核算结果更加准确,并以环境保护目标空气质量达标为原则,核算其大气污染物减排指标,得出具体结论:园区大气污染物SO2、NOX、CO、PM10年排放量分别为449.06t、86.98t、5158.58t、115.06t;无组织排放SO2、CO及PM10的减排比例分别为63.5%、19.2%、42.44%,对应减排量分别为285.16 t/a、990.45 t/a、48.83 t/a。     

Long-term evaluation of coal fly ash and mine tailings co-placement: A site-specific study

This study presents the results of a laboratory investigation conducted to evaluate the efficiency of coal fly ash to control the formation of acid mine drainage (AMD) from mine waste. Site-specific materials, coal fly ash from Atikokan Thermal Generating Station and mine tailings from Musselwhite mine, were mixed at different proportions for the investigation of the drainage chemistry and the optimal mix using static testing (acid–base accounting) and kinetic (column) testing. The acid–base accounting (ABA) results indicated that the fly ash possessed strong alkaline (neutralization) potential (NP) and could be used in the management of reactive mine tailings, thus ensuring prevention of AMD in the long-term. Column tests conducted in the laboratory to further investigate long-term performance of fly ash in the neutralization and prevention of acid mine drainage from tailings similarly showed that mixing fly ash with mine tailings reduces dissolution of many heavy metals from tailings by providing alkalinity to the system. It was found that a fly ash to tailings mass ratio equal to or greater than 15% can effectively prevent AMD generation from Musselwhite mine tailings in the co-placement approach.     

后疫情时期全国碳市场政策对经济和排放的影响

新冠肺炎疫情暴发一年多以来,国际国内形势仍面临极大不确定性。疫情显著降低了短期排放水平,但是经济恢复政策和疫情发展形势的不确定使得对未来经济发展和碳排放的判断变得复杂,碳排放是否会反弹对实现《巴黎协定》目标造成挑战,已经投入运行的全国碳市场能多大程度发挥政策效果等问题值得关注。本文首先定量评估了疫情对经济和环境的短期和长期的影响,基于此分析疫情后全国碳市场政策的实施效果。结果显示:疫情造成中国经济活动以及碳排放短期显著下降,2020年中国全年的GDP增长较基准情景下降3.84%,能源消费和碳排放分别较基准情景下降4.47%和5.12%。但是,接下来的十年内,GDP会逐渐恢复至基准情景,累计碳排放较基准情景增加0.06%。在没有其他气候政策干预的情况下,疫情带来的短期减排效应不足以保障中国各项气候目标的实现,落实碳市场政策十分必要。疫情之后启动全国碳市场,可以促进投资、减少能源使用和碳排放,到2030年,能源消费将下降7.44%,碳排放下降10.49%,碳价为229.73元/t。国外疫情若在2021年还不能得到控制,将会继续影响中国的对外贸易,使得中国GDP损失略有提高,但不会改变国内疫情之后经济恢复的大趋势。     

试论企业文化建设在我国旅游企业中的现状与作用

随着社会的进步和生产力的发展，企业文化作为一种新的管理理念逐渐被人们所接受并越来越受到许多企业家的青睐，本文首先从企业文化中的概念入手，简单阐述企业文化的核心和根本，然后重点分析企业文化在我国旅游企业中的现状和建设过程中存在的问题、原因以及加强企业文化建设的若干对策，最后总结出企业文化的作用．     

现行环境经济核算的局限及建议

随着社会经济发展向着可持续发展战略的转变，资源、环境、经济之间的协调发展日益引起人们的重视，进行环境经济核算已成为最紧迫的现实问题。本文通过分析现行国民经济核算在反映可持续发展要求上的不足和传统环境经济核算的局限性．提出了建立综合的环境核算与经济核算相结合体系的建议和初步框架。     

Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1

Abstract: In this paper, a field‐scale applicability of three forms of artificial neural network algorithms in forecasting short‐term ground‐water levels at specific control points is presented. These algorithms are the feed‐forward back propagation (FFBP), radial basis networks (RBN), and generalized regression networks (GRN). Ground‐water level predictions from these algorithms are in turn to be used in an Optimized Regional Operations Plan that prescribes scheduled wellfield production for the coming four weeks. These models are up against each other for their accuracy of ground‐water level predictions on lead times ranging from a week to four weeks, ease of implementation, and execution times (mainly training time). In total, 208 networks of each of the three algorithms were developed for the study. It is shown that although learning algorithms have emerged as a viable solution at field scale much larger than previously studied, no single algorithm performs consistently better than others on all the criteria. On average, FFBP networks are 20 and 26%, respectively, more accurate than RBN and GRN in forecasting one week ahead water levels and this advantage drops to 5 and 9% accuracy in forecasting four weeks ahead water levels, whereas GRN posted a training time that is only 5% of the training time taken by that of FFBP networks. This may suggest that in field‐scale applications one may have to trade between the type of algorithm to be used and the degree to which a given objective is honored.