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At the end of 2020, the Chinese government announced the pledge to become carbon neutral in the year 2060. Simultaneously, quality growth objectives were established, which were environmentally friendly and promoted the health and wellbeing of the population. The first objective of this study is to assess the gains in energy efficiency and the savings in energy demand that this commitment implies. Secondly, the feasibility of achieving these objectives of savings and efficiency increases is discussed based on an international analysis. The method is based on a quantitative estimate of the primary energy demand throughout the period from 1965 up to the year 2060. For this purpose, long historical series taken from reliable international sources are analyzed. The methodology applied to estimate and project future energy demand is new and based on several steps: The first consists of analyzing the trends of the series and estimating the relationships between them using a robust procedure. Secondly, equilibrium relationships are estimated, which avoids the eventual instabilities involved in the estimation of dynamic models. The third characteristic is based on the bootstrap, estimating and simulating the model by selecting random samples of different sizes from the available dataset. The simulations generate a complete probability distribution for the expected energy demand, which also allows for carrying out a risk analysis, assessing the risk of the demand becoming significantly larger than the expected average. The first result obtained is that the primary energy demand forecast for 2060 is much higher than the demand of the official forecasts by almost three times. However, taking into account the objective to replace 85% of fossil sources with renewables, this discrepancy is greatly reduced and becomes approximately 50% higher than the official forecast. If the savings analyzed in relevant international references are accounted for, then an additional reduction of even up to 40% of this demand could be achieved, so that the final demand would fall further, close to official forecasts. The main and final conclusion is that although the objective of making the Chinese economy carbon neutral by 2060 is feasible, it implies a radical transformation that will necessarily require a determined and unwavering political commitment throughout the entire period considered.

The paper proposes a small area estimation approach that combines small area random effects with a smooth, non-parametrically specified trend. By using penalized splines as the representation for the non-parametric trend, it is possible to express the non-parametric small area estimation problem as a mixed effect model regression. The resulting model is readily fitted by using existing model fitting approaches such as restricted maximum likelihood. We present theoretical results on the prediction mean-squared error of the estimator proposed and on likelihood ratio tests for random effects, and we propose a simple non-parametric bootstrap approach for model inference and estimation of the small area prediction mean-squared error. The applicability of the method is demonstrated on a survey of lakes in north-eastern USA.

Structural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data observed between 1984 and 2020 years by 71 stations and between 1960 and 2020 by eight stations over Thailand. The generalized extreme value (GEV) models are used. Totally, 16 time‐dependent functions of the location and scale parameters of the GEV model are considered. On each station, a model is selected by using Bayesian and Akaike information criteria among these candidates. The return levels corresponding to some years are calculated and predicted for the future. The stations with the highest return levels are Trad, Samui, and Narathiwat, for both AMP1 and AMP2 data. We found some evidence of increasing (decreasing) trends in maximum precipitation for 22 (10) stations in Thailand, based on NS GEV models. Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data by 79 stations over Thailand. The generalized extreme value (GEV) models are used. Totally, 16 time‐dependent functions of the location and scale parameters of the GEV model are considered. On each station, a model is selected by using Bayesian and Akaike information criteria among these candidates. The return levels corresponding to some years are calculated and predicted for the future. The changes of 50‐year “conventional” return levels of AMP1 over Thailand in the years 1990, 2020, and 2050 in which the NS models were selected by BIC. The extreme rainfall in the northwest region, including Mae Sa Riang, and in the northeast region, including Maha Sarakham, is increasing. Whereas maximum precipitation in the central region, including Lop Buri and Phitsanulok, and in Nakon Phanom in the northeast region is decreasing. Downpour in the southeast region, including Nakon Si Thammarat and Narathiwat, is increasing with very heavy precipitation.

Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literature, modeling using state-space models has been extended with the proposal of alternative estimation methods to the maximum likelihood. However, maximum likelihood estimation assumes, as a rule, that the errors are normal. This paper suggests implementing the bootstrap methodology, utilizing the model’s innovation representation, to derive distribution-free estimates—both point and interval—of the parameters in the time-varying state-space model. Additionally, it aims to estimate the standard errors of these parameters through the bootstrap methodology. The simulation study demonstrated that the distribution-free estimation, coupled with the bootstrap methodology, yields point forecasts with a lower mean-squared error, particularly for small time series or when dealing with smaller values of the autoregressive parameter in the state equation of state-space models. In this context, distribution-free estimation with the bootstrap methodology serves as an alternative to maximum likelihood estimation, eliminating the need for distributional assumptions. The application of this methodology to real data showed that it performed well when compared to the usual maximum likelihood estimation and even produced prediction intervals with a similar amplitude for the same level of confidence without any distributional assumptions about the errors.

Under global climate change, flood processes exhibit significant non-stationarity due to multiple driving factors, rendering traditional frequency analysis methods based on stationarity assumptions inadequate for accurate risk assessment. This study, focusing on the Kuitun River Basin and utilizing observed data from the Jiangjunmiao Hydrological Station (1959–2014), develops a joint design approach that addresses both non-stationarity and multivariate dependence. The approach integrates the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) with copula functions and employs a parametric bootstrap to quantify the impacts of marginal parameter estimation and sample size uncertainty on design floods. The results indicate that flooding in the Kuitun River is influenced by precipitation, temperature, and snowmelt, with summer precipitation having the greatest impact. Marginal parameter uncertainty is significantly amplified at high return periods, and the confidence intervals of design values expand as the return period increases. In the joint framework, the OR criterion is more sensitive to parameter perturbations, with the 100-year flood peak and flood volume design values approximately 24.2% and 19.8% higher than those of the AND criterion, respectively. Increasing the sample size significantly reduces uncertainty; when the sample size increases from 56 to 500, the HDR area and confidence interval width decrease by approximately 60–70%, and the stability of joint flood design estimates improves significantly. The research findings can provide a scientific basis and technical support for flood analysis and risk management in the Kuitun River Basin under changing environmental conditions.

The existence of an upper limit for extremes of quantities in the earth sciences, e.g. for river discharge or wind speed, is sometimes suggested. Estimated parameters in extreme-value distributions can assist in interpreting the behaviour of the system. Using simulation, this study investigated how sample size influences the results of statistical tests and related interpretations. Commonly used estimation techniques (maximum likelihood and probability-weighted moments) were employed in a case study; the results were applied in judging time series of annual maximum river flow from two stations on the same river, but with different lengths of observation records. The results revealed that sample size is crucial for determining the existence of an upper bound.

Drought causes serious social and environmental problems that have great impact on the lives of thousands of people all around the world. The purpose of this research was to investigate the trends in humid conditions in the northeast of Brazil (NEB) in the highest climatic precipitation quarters, November–December–January (NDJ), February–March–April (FMA), and May–June–July (MJJ), through the standardized precipitation and evapotranspiration index (SPEI), considering an alternative statistical approach. Precipitation and potential evapotranspiration (PET) time series for the calculation of the SPEI were extracted for the 1794 NEB municipalities between 1980 and 2015 from a grid dataset with a resolution of 0.25° × 0.25° using the bilinear interpolation method. The trends and statistical significance of the SPEI were estimated by quantile regression (QR) and the bootstrap test. In NDJ, opposite trends were seen in the eastern NEB (~0.5 SPEI/decade) and in the south (~−0.6 SPEI/decade). In FMA, most of NEB presented negative trends in the 0.50 and 0.95 quantiles (~−0.3 SPEI/decade), while in MJJ, most of NEB presented positive trends in all quantiles studied (~0.4 SPEI/decade). The results are consistent with observational analyses of extreme rainfall.

Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates.

Purpose Despite the concomitant rise in recent decades in both debt levels (public as well as private) and wealth inequality, empirical evidence on the relationship is absent in existing literature. This is striking especially since recent theoretical contributions point to a link between debt and wealth inequality. We contribute to the debate by investigating empirically whether higher levels of UK public and household debt increase the UK wealth concentration at the top 1 and 10% of the wealth distribution.Design/methodology/approach We employ the Autoregressive Distributed Lag (ARDL) cointegration approach with UK time series data from 1970 to 2019. For robustness, a further analysis using panel data fixed effects estimation on a cross-country sample that also includes France and the USA is undertaken. We also use bootstrapping to conservatively estimate statistical significance.Findings Higher levels of public and household debt are found to increase wealth concentration at the top 1 and 10%. The effect is stronger for household debt. Fixed effects estimation on a cross-country dataset supports the results for the UK.Originality/valueThis study is the first to investigate empirically whether rising levels of UK public and household debt benefit the wealthy and thus widen the gap between the “haves” and “have-nots”.