绿色农田概念内涵与监测评价指标体系

[目的]界定绿色农田定义，明确绿色农田内涵，探讨绿色农田监测评价指标体系，为开展绿色农田监测评价、促进绿色农田建设提供支撑。[方法]文章基于绿色农田建设示范实践，以及文献分析和专家研讨方法，界定绿色农田定义与内涵和指标体系要素构成。[结果]全国多地先后启动了绿色农田建设示范，部分学术文献论述了绿色农田建设的必要性、建设任务与工程措施，但绿色农田定义与内涵和绿色农田指标体系要素构成目前尚未论及。[结论]绿色农田是指土壤肥沃、健康安全、资源高效、宜机耕作、生态良好、景观优美和高产稳产的农田，是高标准农田的“升级版”；绿色农田指标体系由农田土壤环境、农田资源利用高效性、农田宜耕性、生态景观性、高产稳产性5类指标构成。     

Determinants and consequences of student satisfaction in Australian Universities: Evidence from QILT Surveys

We examine the determinants and consequences of student satisfaction, measured by satisfaction scores reported in the QILT surveys from 2012 to 2017. We find that university-level profitability determines overall student satisfaction, where a positive relationship exists between student satisfaction and university performance. This association is more pronounced for Group of Eight (Go8) universities and those with higher academic expenditure. These findings have important implications for higher education providers as the Australian Government is contemplating the use of QILT student satisfaction in allocating public funding for higher education.     

Liquidity indicators,early warning signals in banks,and financial crises

This research compares the performance of three liquidity indicators, namely liquidity ratio (LiqR), liquidity creation (LiqC) and net stable funding difference (NSFD), for sending early warning signals for distressed banks. Recent evidence has shown that LiqR appears incapable of measuring the liquidity condition of banks. However, LiqC and NSFD have not yet been fully examined. Thus, which indicator is more useful in an early warning model becomes an interesting issue. We classify distressed banks as banks that have experienced a bank run, bailout, or failure. Sample data are collected from the United States and the European Union from before and after the financial crisis. We then estimate model predictive value using the sample before the crisis to predict liquidity shortages. Evidence shows that the academic (LiqC) and officially recommended indicators (NSFD) outperform LiqR as early warning signals. Furthermore, LiqC performs best when banks actively engage in income diversification but not fund diversification. Therefore, a well income-diversified bank with high LiqC tends to have high distress probability in the next period.     

Multinomial modeling methods: Predicting four decades of international banking crises

This paper examines banking crises in a large sample of countries over a forty-year period. A multinomial modeling approach is applied to panel data in order to track and capture end-to-end cyclical crisis formations, which enhances the binary focus of previous research studies. Several macroeconomic and banking sector variables are shown to be emblematic of leading indicators across the idiosyncratic stages of a banking crisis. Gross domestic product is an early warning signal across all phases, and a concomitant deterioration in consumption spending and fixed capital formation, preceded by a credit boom, signal a banking crisis to come. Currency depreciation exemplifies ensuing financial distress, reinforced by developmental constructs and regional integration. Lower real interest rates, increasing imports, and rising deposits are frequently harbingers of a recovery. Period effects underscore the dynamic evolution of common contemporaneous precursors over time. Premised on pursuing cyclical movements through multiple outcomes, our findings on forecasting performance suggest enhanced predictive power. Several multinomial logistic models generate higher predictive accuracy in contrast to probit models. Compared to machine learning methods (which encompass artificial neural networks, gradient boost, k-nearest neighbors, and random forests methods), a multinomial logistic approach outperforms during pre-crisis periods and when crisis severity is modeled, whereas gradient boost has the highest predictive accuracy across numerous versions of the multinomial model. As investors and policy makers continue to confront banking crises, leading to high economic and social costs, enhanced multinomial modeling methods make a valuable contribution to improved forecasting performance.     

生态安全观下耕地后备资源评价指标体系探讨

研究目的：针对当前耕地后备资源调查评价中偏重于可耕性而忽视生态安全性的问题，探讨基于生态安全性的耕地后备资源评价指标体系及其构建。研究方法：理论分析与资料论证。研究结果：提出了生态安全观下耕地后备资源评价指标体系的选择原则，构建耕地后备资源的评价指标体系框架。研究结论：耕地后备资源开发利用中不断出现耕地退化问题，说明目前中国耕地已开发过度，若忽视生态安全问题继续开发，新增耕地将会面临更加严重的退化威胁。对耕地后备资源评价不能只注重对耕作可行性的评价，还应考虑开发的生态可行性。因此，耕地后备资源调查评价中应该增加生态安全准则，确保开垦后不发生土地退化及其引发的生态问题。将生态用地作为限制性因素，不列入耕地后备资源。     

CEO performance-based grants’ vesting provisions and debt contracts: Evidence from GAAP,Non-GAAP and KPI metrics

We examine the influence of CEOs’ equity and cash grants’ vesting provisions that are based on (i) accounting performance metrics prepared under US generally accepted principles (GAAP), (ii) non-GAAP performance metrics and (iii) key performance indicators (KPIs) on debt contracts. We find that grants with vesting provisions based on GAAP metrics and KPIs lead to a lower cost of debt, a lower likelihood of collateral requirements and less restrictive covenant terms. In contrast, performance-based grants with non-GAAP vesting provisions lead to a higher cost of debt, a higher likelihood of collateral requirements and more restrictive covenant terms. Supplementary analyses reveal that our results are incremental to other debtholder-friendly features in the CEO contracts, such as grants with debt-related performance measures and CEOs’ inside debt holdings, and robust to alternative variable definitions and specifications. Overall, our results suggest that debtholders understand the differing incentives associated with GAAP, non-GAAP and KPI-based performance measures, and incorporate these differences into debt contracts.     

The making of imperfect indicators for biodiversity: A case study of UK biodiversity performance measurement

This paper seeks to understand the process by which biodiversity performance indicators can be developed. In doing so, this paper examines how biodiversity performance measurements are inherently imperfect and reflects on the implications of that imperfectness. Using document analysis and semi-structured interviews, this research outlines the case of the UK Department for Environment, Food and Rural Affairs and their work in developing biodiversity indicators. Based on the concept of imperfect measurements, this paper outlines the conditions under which imperfect biodiversity indicators can be productive measurements leading to fertile debate and constant improvements, rather than flawed measurements that actors ‘make do’ with. This paper concludes the biodiversity indicators construction process requires a collaboration between a broad set of diverse organisations, including NGOs and research centres. Lastly, this paper outlines the need for ongoing and rigorous review of adopted measurements to reduce the potentially harmful nature of imperfect biodiversity performance measurement.     

华北平原内陆盐碱区玉米品种资源耐盐性综合评价及分类

目的 华北平原内陆盐碱区是咸水补灌导致盐分积累限制玉米产能提升的典型区域，开展该区代表性玉米品种资源耐盐性评价和分类，为促进华北平原盐碱地以种适地科学利用提供理论和技术支撑。方法 文章选择华北平原内陆盐碱区生产中广泛应用的24个玉米品种，获取发芽势、发芽率、发芽指数、活力指数、胚芽长、胚芽重、胚根长、胚根重8个萌发指标，计算相对发芽率、相对发芽势、萌发指数、相对活力指数、相对胚芽长、相对胚芽重、相对胚根长、相对胚根重和盐害指数9项耐盐指标。并利用主成分分析法、加权隶属函数法和聚类分析法开展玉米品种资源耐盐性综合评价与分类。结果 （1）不同玉米品种对盐胁迫的响应有显著差异，其中萌发指标变异范围是5.11 %～84.33 %，耐盐指标的变异范围为3.7 %～56.3 %；（2）通过主成分分析方法将9个耐盐指标降维获得2个相互独立的玉米耐盐性综合评价指标，指标累计贡献率达到了87.35 %；（3）基于加权隶属函数法的各玉米品种耐盐性综合评价指数（D值）变化范围为3.19～16.22；（4）通过聚类分析，将华北平原内陆盐碱区玉米品种资源分为高耐盐品种、中耐盐品种、低耐盐品种和盐敏感品种等4个类型。结论 华北平原内陆盐碱区玉米品种资源耐盐性存在较大差异，基于主成分分析和加权隶属函数法的耐盐性综合评价指数（D值）是评价该区域玉米耐盐特性的有效指标，研究对于实现耐盐玉米品种与盐渍土壤适生匹配具有指导意义。     

Group penalized logistic regressions predict up and down trends for stock prices

Stock prices are influenced by many economic factors, investors psychology and expectations, movement of other stock markets, political events, etc. Therefore, correctly predicting up and down trends for stock prices is an important puzzle in the financial field. In this paper we combine technical analysis with group penalized logistic regressions, and propose group SCAD/MCP penalized logistic regressions with technical indicators to predict up and down trends for stock prices. Firstly, we screen out 24 important technical indicators, divide them into the five different indicator groups, and construct group SCAD/MCP penalized logistic regressions for the three listed companies. Secondly, we apply the training set to learn the parameter estimators and the probability estimators for the two group penalized logistic regressions, adopt the test set to obtain confusion matrices and ROC(Receiver Operating Characteristic) curves to assess their prediction performances, and found that the AUC values to the three companies all exceed 0.78. Finally, we compare group SCAD/MCP penalized logistic regressions with SCAD/MCP penalized logistic regressions, and found that the two group penalized logistic regressions perform better than the two penalized logistic regressions in terms of prediction accuracy and AUC. Therefore, in this paper we develop a new prediction method by combining group SCAD/MCP penalized logistic regressions with technical indicators to improve the prediction accuracy and bring huge economic benefit for investors.     

The diffusion of innovation theory and the effects of IFRS adoption by multinational corporations on capital market performance: a cross-countryanalysis

This article seeks to contribute to International Financial Reporting Standards (IFRS) literature by examining the effects of adopting IFRS on stock market performance worldwide from the diffusion of innovation theory perspective. Our study revealed several interesting findings after using combinations of unique panel data sets from 110 countries worldwide and conducting a robust empirical analysis from 1995 to 2014. First, we found a positive association between late mandatory IFRS adoption and stock market integration in Europe. Second, our findings indicate a significant negative association between early IFRS adoption and the following financial indicators: stock market trading volumes, stock market capitalization, market turnover, and market return. Third, our study reveals an insignificant association between early IFRS adoption and stock price volatility alongside stock market development. Our findings are robust and have significant practical and policy implications for regulators and policymakers of multinational corporations.