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Our brains are not wired to contend with the pace of technological change: These respondents said the rising speed, reach and efficiencies of the internet and emerging online applications will magnify these human tendencies and that technology-based solutions will not be able to overcome them. They predicted a future information landscape in which fake information crowds out reliable information. Some even foresaw a world in which widespread information scams and mass manipulation cause broad swathes of public to simply give up on being informed participants in civic life.

A number of these experts said solutions such as tagging, flagging or other labeling of questionable content will continue to expand and be of further use in the future in tackling the propagation of misinformation

Our science-based approach, compatible with the EU post-2020 climate legislation, helps to ensure that only genuine deviations from the continuation of historically documented forest management practices are accounted toward climate targets, therefore enhancing the consistency and comparability across GHG sectors. It provides flexibility for countries to increase harvest in future reference levels when justified by age-related dynamics. It offers a policy-neutral solution to the polarized debate on forest accounting (especially on bioenergy) and supports the credibility of forest sector mitigation under the Paris Agreement.

Assessing the mitigation outcomes in the forest sector is more complex than in other GHG sectors (e.g., energy, agriculture). This is because it can be hard to disentangle the simultaneous natural and anthropogenic processes that determine forest-related fluxes. Moreover, unlike other sectors, future emissions and removals in forests can change over time as a result of forest characteristics such as age-class distributions, which are largely determined by past forest management and natural disturbances [7].

Our method, as any modeled projection, contains uncertainties, mainly related to the original input data and to methodological assumptions. Different factors, such as initial age class distribution (i.e., at the beginning of the model run), past natural disturbances (fires and storms), the criteria and timing for thinnings and final cuts, the share of harvest between different silvicultural operations and between different species, may considerably affect the projected age class distribution and, as a consequence, the future amount of harvest [38]. Other sources of uncertainty are the future impact of natural disturbances [39] and of climate change or atmospheric CO2 [36], not addressed in our study.

This approach builds on documentable and reviewable past management practices (that should be defined by the country), fully reflects the country-specific age-related forestry dynamics, and does not include unreviewable assumptions about the future impacts of policies. In other words, our approach is based on the supply-side deterministic evolution of forest resources, but ignores the demand-side dynamics (i.e. possible future impact of policies and markets).

Step 4.2. Here we summarize the procedure to calculate the carbon losses due to future harvest expected under the continuation of the management practices (for other losses and non-CO2 emissions, see [30]). For each stratum and management practice, the following sub-steps need to be implemented (see Fig. 5).

Calculate the future harvest during the CP (H CP ), by multiplying the historical harvest fraction (Eq. 1) by the expected biomass available in the CP (BAWSCP), for each stratum and management practice:

The CBM is an inventory-based, yield-curve-driven model that simulates the stand- and landscape-level C dynamics of above- and belowground biomass, dead organic matter (DOM; litter and dead wood) and mineral soil. The model has been already implemented at the EU level to estimate the forest C dynamics from 2000 to 2012 [52] and the future carbon budget and fluxes under different management scenarios to 2030 [38]. The main input data come from National Forest Inventories (NFIs, see [30, 38, 53]). Here we apply the same methods, data and assumptions used in these studies. The spatial framework applied by the CBM conceptually follows IPCC reporting method 1 [10], in which the spatial units are defined by their geographic boundaries and all forest stands are geographically referenced to a spatial unit (SPU). The intersection between 26 administrative units (i.e., European countries) and 36 climatic units yielded 910 SPUs. Within a SPU, each forest stand is characterized by age, area and seven classifiers that provide administrative and ecological information: the link to the appropriate yield curves; the parameters defining the silvicultural system, such as the forest composition (defined according to different forest types, FTs) and the management type (MT). From the NFIs of each country, we derived (i) the country-specific original age-class distribution (for the even-aged forests), (ii) the main FTs based on the forest composition, (iii) the average volume and current annual increment (if possible, defined for each FT), and (iv) the main MTs. The MT parameters may include even-aged high forests, uneven-aged high forests, coppices and specific silvicultural systems such as clear cuts (with different rotation lengths for each FT), thinnings, shelterwood systems, partial cuttings, etc. In a few cases, because of the lack of country-specific information, some of these parameters were derived either from the literature or from average values reported for other countries. Additional methodological details and country-level input data may be found in [32, 52, 54].

We also conducted an extensive review of existing literature to identify potential options to sustain Medicare for the future. The report includes many options described or endorsed by the National Commission on Fiscal Responsibility and Reform (the Simpson-Bowles commission), the Bipartisan Policy Center Task Force on Deficit Reduction, the Medicare Payment Advisory Commission (MedPAC), the Congressional Budget Office (CBO), and many others. We also worked with a team of seasoned policy experts who fleshed out these concepts and ideas for inclusion in this report to present a thorough explanation of the context, impacts, and, when available, potential savings. In particular, we would like to acknowledge Robert Berenson for making significant contributions to several parts of this report, and Leslie Aronovitz, Randall Brown, Judy Feder, Jessie Gruman, Jack Hoadley, Andy Schneider, and Shoshanna Sofaer for their contributions to specific topic areas. We also would like to acknowledge Chad Boult, Susan Bartlett Foote, Richard Frank, Joanne Lynn, Robert Mechanic, Diane Meier, Peter Neumann, Joseph Ouslander, Earl Steinberg, George Taler, and Sean Tunis for their participation in small-group discussions related to specific topics covered in this report, and Actuarial Research Corporation (ARC) for providing cost estimates and distributional analysis of several options. Technical support in the preparation of this report was provided by Health Policy Alternatives, Inc. We are indebted to Richard Sorian for bringing to this project his keen policy insight and skillful editorial assistance.

The aging of the Baby Boom generation not only makes millions of Americans newly eligible for Medicare, it also reduces the number of workers paying the Medicare payroll tax, a primary source of revenue for the Medicare Part A Hospital Insurance (HI) trust fund. The HI trust fund currently is projected to be solvent through 2024, but will have insufficient funds to pay full benefits beyond that point (Boards of Trustees 2012). In the past, Congress has taken steps to maintain and extend the solvency of the HI trust fund by restraining growth in Medicare spending and increasing payroll tax revenue, and will need to take action to extend the life of the trust fund at some point in the future to fully fund current benefits.

Furthermore, Blockchain can be used to establish great digital control mechanisms that will make old economic and political processes obsolete. The technology allows cryptocurrencies to be developed for specific areas of application. And so, cryptocurrencies can be exchanged between individual areas of application as required. In this flexibility I see a highly attractive model for the future.

As businesses evolve, however, our roles within these companies must change. As we move into the future of HR, this means that human resources professionals need to become credible activists (among other key competencies) in order to get the attention of business leaders and to do their jobs more effectively.

As many in the environmental community gear up for the UN high-level meeting Stockholm+50 on 2-3 June, which will mark fifty years since the UN's first environmental conference, the Global Future Council on the Net-Zero Transition is releasing two White Papers examining how industry can be at the forefront of building a low-carbon future.

The future of 5G/6G network resiliency, security, and 6G standards development currently is uncertain and both factors face multiple potential scenarios that will be important for DHS and other organization leaders to monitor to inform future decision-making.

One area I will be closely looking at OCI in 2022 is its integration of Arm-based instances to lower its customers cost of compute. Cloud companies like AWS have fully embraced the technology and Oracle has to watch this carefully. I will also be looking at composable memory architectures in a future version of OCI where customers can add more memory on-demand without having to add a compute instance.

Macquarie University, a public research university in Sydney, Australia prides itself on being an innovative and progressive university, challenging conventional thinking around higher education delivery. It was quick to recognize the seismic shifts occurring in its operating environment and that it needed to change rapidly to continue to survive and thrive in future. 2b1af7f3a8